feat: add tensor parallelism support for Step 3.5 Flash

Add Step3p5ShardingStrategy to auto_parallel.py following the
DeepSeek pattern (shared expert + routed experts). Shard attention
q/k/v/o projections across devices and MoE expert weights in-place
with all-reduce synchronization.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.github/workflows/pipeline.yml

@@ -142,4 +142,4 @@ jobs:
           # Run pytest outside sandbox (needs GPU access for MLX)
           export HOME="$RUNNER_TEMP"
           export EXO_TESTS=1
-          $TEST_ENV/bin/python -m pytest src -m "not slow" --import-mode=importlib
+          EXO_RESOURCES_DIR="$PWD/resources" $TEST_ENV/bin/python -m pytest src -m "not slow" --import-mode=importlib

packaging/pyinstaller/exo.spec

@@ -10,6 +10,7 @@ PROJECT_ROOT = Path.cwd()
 SOURCE_ROOT = PROJECT_ROOT / "src"
 ENTRYPOINT = SOURCE_ROOT / "exo" / "__main__.py"
 DASHBOARD_DIR = PROJECT_ROOT / "dashboard" / "build"
+RESOURCES_DIR = PROJECT_ROOT / "resources"
 EXO_SHARED_MODELS_DIR = SOURCE_ROOT / "exo" / "shared" / "models"
 
 if not ENTRYPOINT.is_file():
@@ -18,6 +19,9 @@ if not ENTRYPOINT.is_file():
 if not DASHBOARD_DIR.is_dir():
     raise SystemExit(f"Dashboard assets are missing: {DASHBOARD_DIR}")
 
+if not RESOURCES_DIR.is_dir():
+    raise SystemExit(f"Resource assets are missing: {RESOURCES_DIR}")
+
 if not EXO_SHARED_MODELS_DIR.is_dir():
     raise SystemExit(f"Shared model assets are missing: {EXO_SHARED_MODELS_DIR}")
 
@@ -58,6 +62,7 @@ HIDDEN_IMPORTS = sorted(
 
 DATAS: list[tuple[str, str]] = [
     (str(DASHBOARD_DIR), "dashboard"),
+    (str(RESOURCES_DIR), "resources"),
     (str(MLX_LIB_DIR), "mlx/lib"),
     (str(EXO_SHARED_MODELS_DIR), "exo/shared/models"),
 ]

pyproject.toml

@@ -6,6 +6,8 @@ readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
     "aiofiles>=24.1.0",
+    "aiohttp>=3.12.14",
+    "types-aiofiles>=24.1.0.20250708",
     "pydantic>=2.11.7",
     "fastapi>=0.116.1",
     "filelock>=3.18.0",

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-Krea-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-Krea-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15475325472
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5950704160
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21426029632
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11901408320
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-dev.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-dev"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33327437952
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23802816640
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-4bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-4bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 15470210592
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 5945589280
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell-8bit.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell-8bit"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 21415799872
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 11891178560
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--FLUX.1-schnell.toml

@@ -0,0 +1,45 @@
+model_id = "exolabs/FLUX.1-schnell"
+n_layers = 57
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 33306978432
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0
+
+[[components]]
+component_name = "text_encoder_2"
+component_path = "text_encoder_2/"
+n_layers = 24
+can_shard = false
+safetensors_index_filename = "model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 9524621312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 57
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 23782357120
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-4bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-4bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 26799533856
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 10215200544
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509-8bit.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509-8bit"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 37014734400
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 20430401088
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image-Edit-2509.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image-Edit-2509"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["ImageToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/image_model_cards/exolabs--Qwen-Image.toml

@@ -0,0 +1,35 @@
+model_id = "exolabs/Qwen-Image"
+n_layers = 60
+hidden_size = 1
+supports_tensor = false
+tasks = ["TextToImage"]
+
+[storage_size]
+in_bytes = 57445135488
+
+[[components]]
+component_name = "text_encoder"
+component_path = "text_encoder/"
+n_layers = 12
+can_shard = false
+
+[components.storage_size]
+in_bytes = 16584333312
+
+[[components]]
+component_name = "transformer"
+component_path = "transformer/"
+n_layers = 60
+can_shard = true
+safetensors_index_filename = "diffusion_pytorch_model.safetensors.index.json"
+
+[components.storage_size]
+in_bytes = 40860802176
+
+[[components]]
+component_name = "vae"
+component_path = "vae/"
+can_shard = false
+
+[components.storage_size]
+in_bytes = 0

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/DeepSeek-V3.1-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 405874409472

resources/inference_model_cards/mlx-community--DeepSeek-V3.1-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/DeepSeek-V3.1-8bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 765577920512

resources/inference_model_cards/mlx-community--GLM-4.5-Air-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.5-Air-8bit"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 122406567936

resources/inference_model_cards/mlx-community--GLM-4.5-Air-bf16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.5-Air-bf16"
+n_layers = 46
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 229780750336

resources/inference_model_cards/mlx-community--GLM-4.7-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-4bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 198556925568

resources/inference_model_cards/mlx-community--GLM-4.7-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-6bit"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 286737579648

resources/inference_model_cards/mlx-community--GLM-4.7-8bit-gs32.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-8bit-gs32"
+n_layers = 91
+hidden_size = 5120
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 396963397248

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-4bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 19327352832

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-5bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-5bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 22548578304

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-6bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 26843545600

resources/inference_model_cards/mlx-community--GLM-4.7-Flash-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/GLM-4.7-Flash-8bit"
+n_layers = 47
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 34359738368

resources/inference_model_cards/mlx-community--Kimi-K2-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Instruct-4bit"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 620622774272

resources/inference_model_cards/mlx-community--Kimi-K2-Thinking.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2-Thinking"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 706522120192

resources/inference_model_cards/mlx-community--Kimi-K2.5.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Kimi-K2.5"
+n_layers = 61
+hidden_size = 7168
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 662498705408

resources/inference_model_cards/mlx-community--Llama-3.2-1B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-1B-Instruct-4bit"
+n_layers = 16
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 729808896

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-4bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 1863319552

resources/inference_model_cards/mlx-community--Llama-3.2-3B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.2-3B-Instruct-8bit"
+n_layers = 28
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 3501195264

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

resources/inference_model_cards/mlx-community--Llama-3.3-70B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Llama-3.3-70B-Instruct-8bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 76799803392

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-70B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 40652242944

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 4637851648

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 8954839040

resources/inference_model_cards/mlx-community--Meta-Llama-3.1-8B-Instruct-bf16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"
+n_layers = 32
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 16882073600

resources/inference_model_cards/mlx-community--MiniMax-M2.1-3bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/MiniMax-M2.1-3bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 100086644736

resources/inference_model_cards/mlx-community--MiniMax-M2.1-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/MiniMax-M2.1-8bit"
+n_layers = 61
+hidden_size = 3072
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 242986745856

resources/inference_model_cards/mlx-community--Qwen3-0.6B-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-0.6B-4bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 342884352

resources/inference_model_cards/mlx-community--Qwen3-0.6B-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-0.6B-8bit"
+n_layers = 28
+hidden_size = 1024
+supports_tensor = false
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 698351616

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 141733920768

resources/inference_model_cards/mlx-community--Qwen3-235B-A22B-Instruct-2507-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"
+n_layers = 94
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 268435456000

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-30B-A3B-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 17612931072

resources/inference_model_cards/mlx-community--Qwen3-30B-A3B-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-30B-A3B-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 33279705088

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 289910292480

resources/inference_model_cards/mlx-community--Qwen3-Coder-480B-A35B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"
+n_layers = 62
+hidden_size = 6144
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 579820584960

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 46976204800

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Instruct-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 88814387200

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 47080074240

resources/inference_model_cards/mlx-community--Qwen3-Next-80B-A3B-Thinking-8bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"
+n_layers = 48
+hidden_size = 2048
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 88814387200

resources/inference_model_cards/mlx-community--Step-3.5-Flash-4bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-4bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 114572190076

resources/inference_model_cards/mlx-community--Step-3.5-Flash-6bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-6bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 159039627774

resources/inference_model_cards/mlx-community--Step-3.5-Flash-8Bit.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/Step-3.5-Flash-8Bit"
+n_layers = 45
+hidden_size = 4096
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 209082699847

resources/inference_model_cards/mlx-community--gpt-oss-120b-MXFP4-Q8.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-120b-MXFP4-Q8"
+n_layers = 36
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 70652212224

resources/inference_model_cards/mlx-community--gpt-oss-20b-MXFP4-Q8.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/gpt-oss-20b-MXFP4-Q8"
+n_layers = 24
+hidden_size = 2880
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 12025908224

resources/inference_model_cards/mlx-community--llama-3.3-70b-instruct-fp16.toml

@@ -0,0 +1,8 @@
+model_id = "mlx-community/llama-3.3-70b-instruct-fp16"
+n_layers = 80
+hidden_size = 8192
+supports_tensor = true
+tasks = ["TextGeneration"]
+
+[storage_size]
+in_bytes = 144383672320

src/exo/download/coordinator.py

@@ -121,7 +121,6 @@ class DownloadCoordinator:
     def _start_download_task(
         self, shard: ShardMetadata, initial_progress: RepoDownloadProgress
     ) -> None:
-        logger.warning("starting download for {shard}")
         model_id = shard.model_card.model_id
 
         # Emit ongoing status

src/exo/download/download_utils.py

@@ -8,13 +8,13 @@ import traceback
 from collections.abc import Awaitable
 from datetime import timedelta
 from pathlib import Path
-from typing import Callable, Literal, cast
+from typing import Callable, Literal
 from urllib.parse import urljoin
 
 import aiofiles
 import aiofiles.os as aios
+import aiohttp
 import certifi
-import httpx
 from huggingface_hub import (
     snapshot_download,  # pyright: ignore[reportUnknownVariableType]
 )
@@ -176,7 +176,7 @@ async def fetch_file_list_with_cache(
         # Fetch failed - try cache fallback
         if await aios.path.exists(cache_file):
             logger.warning(
-                f"{type(e).__name__}: Failed to fetch file list for {model_id}, using cached data"
+                f"Failed to fetch file list for {model_id}, using cached data: {e}"
             )
             async with aiofiles.open(cache_file, "r") as f:
                 return TypeAdapter(list[FileListEntry]).validate_json(await f.read())
@@ -196,7 +196,7 @@ async def fetch_file_list_with_retry(
         except Exception as e:
             if attempt == n_attempts - 1:
                 raise e
-            await asyncio.sleep(min(16, 0.5 * float(2.0 ** int(attempt))))
+            await asyncio.sleep(min(8, 0.1 * float(2.0 ** int(attempt))))
     raise Exception(
         f"Failed to fetch file list for {model_id=} {revision=} {path=} {recursive=}"
     )
@@ -211,25 +211,26 @@ async def _fetch_file_list(
     headers = await get_download_headers()
     async with (
         create_http_session(timeout_profile="short") as session,
+        session.get(url, headers=headers) as response,
     ):
-        response = await session.get(url, headers=headers)
-        if response.status_code in [401, 403]:
-            msg = await _build_auth_error_message(response.status_code, model_id)
+        if response.status in [401, 403]:
+            msg = await _build_auth_error_message(response.status, model_id)
             raise HuggingFaceAuthenticationError(msg)
-        if response.status_code != 200:
-            raise Exception(f"Failed to fetch file list: {response.status_code}")
-
-        data = TypeAdapter(list[FileListEntry]).validate_json(response.text)
-        files: list[FileListEntry] = []
-        for item in data:
-            if item.type == "file":
-                files.append(FileListEntry.model_validate(item))
-            elif item.type == "directory" and recursive:
-                subfiles = await _fetch_file_list(
-                    model_id, revision, item.path, recursive
-                )
-                files.extend(subfiles)
-        return files
+        if response.status == 200:
+            data_json = await response.text()
+            data = TypeAdapter(list[FileListEntry]).validate_json(data_json)
+            files: list[FileListEntry] = []
+            for item in data:
+                if item.type == "file":
+                    files.append(FileListEntry.model_validate(item))
+                elif item.type == "directory" and recursive:
+                    subfiles = await _fetch_file_list(
+                        model_id, revision, item.path, recursive
+                    )
+                    files.extend(subfiles)
+            return files
+        else:
+            raise Exception(f"Failed to fetch file list: {response.status}")
 
 
 async def get_download_headers() -> dict[str, str]:
@@ -237,29 +238,34 @@ async def get_download_headers() -> dict[str, str]:
 
 
 def create_http_session(
+    auto_decompress: bool = False,
     timeout_profile: Literal["short", "long"] = "long",
-) -> httpx.AsyncClient:
+) -> aiohttp.ClientSession:
     if timeout_profile == "short":
         total_timeout = 30
         connect_timeout = 10
-        read_timeout = 30
+        sock_read_timeout = 30
+        sock_connect_timeout = 10
     else:
         total_timeout = 1800
         connect_timeout = 60
-        read_timeout = 1800
+        sock_read_timeout = 1800
+        sock_connect_timeout = 60
 
     ssl_context = ssl.create_default_context(
         cafile=os.getenv("SSL_CERT_FILE") or certifi.where()
     )
+    connector = aiohttp.TCPConnector(ssl=ssl_context)
 
-    # default here is to load env vars
-    return httpx.AsyncClient(
-        verify=ssl_context,
-        timeout=httpx.Timeout(
+    return aiohttp.ClientSession(
+        auto_decompress=auto_decompress,
+        connector=connector,
+        proxy=os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY") or None,
+        timeout=aiohttp.ClientTimeout(
+            total=total_timeout,
             connect=connect_timeout,
-            read=read_timeout,
-            write=total_timeout,
-            pool=total_timeout,
+            sock_read=sock_read_timeout,
+            sock_connect=sock_connect_timeout,
         ),
     )
 
@@ -286,28 +292,26 @@ async def file_meta(
     headers = await get_download_headers()
     async with (
         create_http_session(timeout_profile="short") as session,
-        session.stream("HEAD", url, headers=headers) as r,
+        session.head(url, headers=headers) as r,
     ):
-        if r.status_code == 307:
+        if r.status == 307:
             # On redirect, only trust Hugging Face's x-linked-* headers.
-            x_linked_size = cast(str | None, r.headers.get("x-linked-size"))
-            x_linked_etag = cast(str | None, r.headers.get("x-linked-etag"))
+            x_linked_size = r.headers.get("x-linked-size")
+            x_linked_etag = r.headers.get("x-linked-etag")
             if x_linked_size and x_linked_etag:
                 content_length = int(x_linked_size)
                 etag = trim_etag(x_linked_etag)
                 return content_length, etag
             # Otherwise, follow the redirect to get authoritative size/hash
-            redirected_location = cast(str | None, r.headers.get("location"))
+            redirected_location = r.headers.get("location")
             return await file_meta(model_id, revision, path, redirected_location)
-        if r.status_code in [401, 403]:
-            msg = await _build_auth_error_message(r.status_code, model_id)
+        if r.status in [401, 403]:
+            msg = await _build_auth_error_message(r.status, model_id)
             raise HuggingFaceAuthenticationError(msg)
-        content_length = cast(
-            str | None,
-            r.headers.get("x-linked-size") or r.headers.get("content-length"),
+        content_length = int(
+            r.headers.get("x-linked-size") or r.headers.get("content-length") or 0
         )
-        content_length = 0 if content_length is None else int(content_length)
-        etag = cast(str | None, r.headers.get("x-linked-etag") or r.headers.get("etag"))
+        etag = r.headers.get("x-linked-etag") or r.headers.get("etag")
         assert content_length > 0, f"No content length for {url}"
         assert etag is not None, f"No remote hash for {url}"
         etag = trim_etag(etag)
@@ -336,7 +340,7 @@ async def download_file_with_retry(
                 f"Download error on attempt {attempt}/{n_attempts} for {model_id=} {revision=} {path=} {target_dir=}"
             )
             logger.error(traceback.format_exc())
-            await asyncio.sleep(min(16, 0.5 * (2.0**attempt)))
+            await asyncio.sleep(min(8, 0.1 * (2.0**attempt)))
     raise Exception(
         f"Failed to download file {model_id=} {revision=} {path=} {target_dir=}"
     )
@@ -349,7 +353,6 @@ async def _download_file(
     target_dir: Path,
     on_progress: Callable[[int, int, bool], None] = lambda _, __, ___: None,
 ) -> Path:
-    logger.warning(f"downloading {path} from {model_id} to {target_dir}")
     target_path = target_dir / path
 
     if await aios.path.exists(target_path):
@@ -389,20 +392,20 @@ async def _download_file(
         n_read = resume_byte_pos or 0
         async with (
             create_http_session(timeout_profile="long") as session,
-            session.stream("GET", url, headers=headers, follow_redirects=True) as r,
+            session.get(url, headers=headers) as r,
         ):
-            if r.status_code == 404:
+            if r.status == 404:
                 raise FileNotFoundError(f"File not found: {url}")
-            if r.status_code in [401, 403]:
-                msg = await _build_auth_error_message(r.status_code, model_id)
+            if r.status in [401, 403]:
+                msg = await _build_auth_error_message(r.status, model_id)
                 raise HuggingFaceAuthenticationError(msg)
-            assert r.status_code in [200, 206], (
-                f"Failed to download {path} from {url}: {r.status_code}"
+            assert r.status in [200, 206], (
+                f"Failed to download {path} from {url}: {r.status}"
             )
             async with aiofiles.open(
                 partial_path, "ab" if resume_byte_pos else "wb"
             ) as f:
-                async for chunk in r.aiter_bytes(8 * 1024 * 1024):
+                while chunk := await r.content.read(8 * 1024 * 1024):
                     n_read = n_read + (await f.write(chunk))
                     on_progress(n_read, length, False)
 

src/exo/download/impl_shard_downloader.py

@@ -7,7 +7,7 @@ from loguru import logger
 
 from exo.download.download_utils import RepoDownloadProgress, download_shard
 from exo.download.shard_downloader import ShardDownloader
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.shared.types.worker.shards import (
     PipelineShardMetadata,
     ShardMetadata,
@@ -160,14 +160,13 @@ class ResumableShardDownloader(ShardDownloader):
         # Kick off download status coroutines concurrently
         tasks = [
             asyncio.create_task(_status_for_model(model_card.model_id))
-            for model_card in MODEL_CARDS.values()
+            for model_card in await get_model_cards()
         ]
 
         for task in asyncio.as_completed(tasks):
             try:
                 yield await task
             except Exception as e:
-                task.cancel()
                 logger.warning(f"Error downloading shard: {type(e).__name__}")
 
     async def get_shard_download_status_for_shard(

src/exo/master/api.py

@@ -40,6 +40,7 @@ from exo.master.image_store import ImageStore
 from exo.master.placement import place_instance as get_instance_placements
 from exo.shared.apply import apply
 from exo.shared.constants import (
+    DASHBOARD_DIR,
     EXO_IMAGE_CACHE_DIR,
     EXO_MAX_CHUNK_SIZE,
     EXO_TRACING_CACHE_DIR,
@@ -47,9 +48,9 @@ from exo.shared.constants import (
 from exo.shared.election import ElectionMessage
 from exo.shared.logging import InterceptLogger
 from exo.shared.models.model_cards import (
-    MODEL_CARDS,
     ModelCard,
     ModelId,
+    get_model_cards,
 )
 from exo.shared.tracing import TraceEvent, compute_stats, export_trace, load_trace_file
 from exo.shared.types.api import (
@@ -138,7 +139,6 @@ from exo.shared.types.worker.instances import Instance, InstanceId, InstanceMeta
 from exo.shared.types.worker.shards import Sharding
 from exo.utils.banner import print_startup_banner
 from exo.utils.channels import Receiver, Sender, channel
-from exo.utils.dashboard_path import find_dashboard
 from exo.utils.event_buffer import OrderedBuffer
 
 
@@ -146,18 +146,6 @@ def _format_to_content_type(image_format: Literal["png", "jpeg", "webp"] | None)
     return f"image/{image_format or 'png'}"
 
 
-async def resolve_model_card(model_id: ModelId) -> ModelCard:
-    if model_id in MODEL_CARDS:
-        model_card = MODEL_CARDS[model_id]
-        return model_card
-
-    for card in MODEL_CARDS.values():
-        if card.model_id == ModelId(model_id):
-            return card
-
-    return await ModelCard.from_hf(model_id)
-
-
 class API:
     def __init__(
         self,
@@ -204,7 +192,7 @@ class API:
         self.app.mount(
             "/",
             StaticFiles(
-                directory=find_dashboard(),
+                directory=DASHBOARD_DIR,
                 html=True,
             ),
             name="dashboard",
@@ -381,10 +369,7 @@ class API:
         if len(list(self.state.topology.list_nodes())) == 0:
             return PlacementPreviewResponse(previews=[])
 
-        cards = [card for card in MODEL_CARDS.values() if card.model_id == model_id]
-        if not cards:
-            raise HTTPException(status_code=404, detail=f"Model {model_id} not found")
-
+        model_card = await ModelCard.load(model_id)
         instance_combinations: list[tuple[Sharding, InstanceMeta, int]] = []
         for sharding in (Sharding.Pipeline, Sharding.Tensor):
             for instance_meta in (InstanceMeta.MlxRing, InstanceMeta.MlxJaccl):
@@ -399,96 +384,93 @@ class API:
         # TODO: PDD
         # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1))
 
-        for model_card in cards:
-            for sharding, instance_meta, min_nodes in instance_combinations:
-                try:
-                    placements = get_instance_placements(
-                        PlaceInstance(
-                            model_card=model_card,
-                            sharding=sharding,
-                            instance_meta=instance_meta,
-                            min_nodes=min_nodes,
-                        ),
-                        node_memory=self.state.node_memory,
-                        node_network=self.state.node_network,
-                        topology=self.state.topology,
-                        current_instances=self.state.instances,
-                        required_nodes=required_nodes,
-                    )
-                except ValueError as exc:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error=str(exc),
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                current_ids = set(self.state.instances.keys())
-                new_instances = [
-                    instance
-                    for instance_id, instance in placements.items()
-                    if instance_id not in current_ids
-                ]
-
-                if len(new_instances) != 1:
-                    if (model_card.model_id, sharding, instance_meta, 0) not in seen:
-                        previews.append(
-                            PlacementPreview(
-                                model_id=model_card.model_id,
-                                sharding=sharding,
-                                instance_meta=instance_meta,
-                                instance=None,
-                                error="Expected exactly one new instance from placement",
-                            )
-                        )
-                    seen.add((model_card.model_id, sharding, instance_meta, 0))
-                    continue
-
-                instance = new_instances[0]
-                shard_assignments = instance.shard_assignments
-                placement_node_ids = list(shard_assignments.node_to_runner.keys())
-
-                memory_delta_by_node: dict[str, int] = {}
-                if placement_node_ids:
-                    total_bytes = model_card.storage_size.in_bytes
-                    per_node = total_bytes // len(placement_node_ids)
-                    remainder = total_bytes % len(placement_node_ids)
-                    for index, node_id in enumerate(
-                        sorted(placement_node_ids, key=str)
-                    ):
-                        extra = 1 if index < remainder else 0
-                        memory_delta_by_node[str(node_id)] = per_node + extra
-
-                if (
-                    model_card.model_id,
-                    sharding,
-                    instance_meta,
-                    len(placement_node_ids),
-                ) not in seen:
+        for sharding, instance_meta, min_nodes in instance_combinations:
+            try:
+                placements = get_instance_placements(
+                    PlaceInstance(
+                        model_card=model_card,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        min_nodes=min_nodes,
+                    ),
+                    node_memory=self.state.node_memory,
+                    node_network=self.state.node_network,
+                    topology=self.state.topology,
+                    current_instances=self.state.instances,
+                    required_nodes=required_nodes,
+                )
+            except ValueError as exc:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
                     previews.append(
                         PlacementPreview(
                             model_id=model_card.model_id,
                             sharding=sharding,
                             instance_meta=instance_meta,
-                            instance=instance,
-                            memory_delta_by_node=memory_delta_by_node or None,
-                            error=None,
+                            instance=None,
+                            error=str(exc),
                         )
                     )
-                seen.add(
-                    (
-                        model_card.model_id,
-                        sharding,
-                        instance_meta,
-                        len(placement_node_ids),
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            current_ids = set(self.state.instances.keys())
+            new_instances = [
+                instance
+                for instance_id, instance in placements.items()
+                if instance_id not in current_ids
+            ]
+
+            if len(new_instances) != 1:
+                if (model_card.model_id, sharding, instance_meta, 0) not in seen:
+                    previews.append(
+                        PlacementPreview(
+                            model_id=model_card.model_id,
+                            sharding=sharding,
+                            instance_meta=instance_meta,
+                            instance=None,
+                            error="Expected exactly one new instance from placement",
+                        )
+                    )
+                seen.add((model_card.model_id, sharding, instance_meta, 0))
+                continue
+
+            instance = new_instances[0]
+            shard_assignments = instance.shard_assignments
+            placement_node_ids = list(shard_assignments.node_to_runner.keys())
+
+            memory_delta_by_node: dict[str, int] = {}
+            if placement_node_ids:
+                total_bytes = model_card.storage_size.in_bytes
+                per_node = total_bytes // len(placement_node_ids)
+                remainder = total_bytes % len(placement_node_ids)
+                for index, node_id in enumerate(sorted(placement_node_ids, key=str)):
+                    extra = 1 if index < remainder else 0
+                    memory_delta_by_node[str(node_id)] = per_node + extra
+
+            if (
+                model_card.model_id,
+                sharding,
+                instance_meta,
+                len(placement_node_ids),
+            ) not in seen:
+                previews.append(
+                    PlacementPreview(
+                        model_id=model_card.model_id,
+                        sharding=sharding,
+                        instance_meta=instance_meta,
+                        instance=instance,
+                        memory_delta_by_node=memory_delta_by_node or None,
+                        error=None,
                     )
                 )
+            seen.add(
+                (
+                    model_card.model_id,
+                    sharding,
+                    instance_meta,
+                    len(placement_node_ids),
+                )
+            )
 
         return PlacementPreviewResponse(previews=previews)
 
@@ -652,23 +634,21 @@ class API:
         response = await self._collect_text_generation_with_stats(command.command_id)
         return response
 
-    async def _resolve_and_validate_text_model(self, model: ModelId) -> ModelId:
+    async def _resolve_and_validate_text_model(self, model_id: ModelId) -> ModelId:
         """Validate a text model exists and return the resolved model ID.
 
         Raises HTTPException 404 if no instance is found for the model.
         """
-        model_card = await resolve_model_card(model)
-        resolved = model_card.model_id
         if not any(
-            instance.shard_assignments.model_id == resolved
+            instance.shard_assignments.model_id == model_id
             for instance in self.state.instances.values()
         ):
-            await self._trigger_notify_user_to_download_model(resolved)
+            await self._trigger_notify_user_to_download_model(model_id)
             raise HTTPException(
                 status_code=404,
-                detail=f"No instance found for model {resolved}",
+                detail=f"No instance found for model {model_id}",
             )
-        return resolved
+        return model_id
 
     async def _validate_image_model(self, model: ModelId) -> ModelId:
         """Validate model exists and return resolved model ID.
@@ -1237,7 +1217,7 @@ class API:
                     supports_tensor=card.supports_tensor,
                     tasks=[task.value for task in card.tasks],
                 )
-                for card in MODEL_CARDS.values()
+                for card in await get_model_cards()
             ]
         )
 

src/exo/shared/constants.py

@@ -2,6 +2,8 @@ import os
 import sys
 from pathlib import Path
 
+from exo.utils.dashboard_path import find_dashboard, find_resources
+
 _EXO_HOME_ENV = os.environ.get("EXO_HOME", None)
 
 
@@ -31,6 +33,14 @@ EXO_MODELS_DIR = (
     if _EXO_MODELS_DIR_ENV is None
     else Path.home() / _EXO_MODELS_DIR_ENV
 )
+_RESOURCES_DIR_ENV = os.environ.get("EXO_RESOURCES_DIR", None)
+RESOURCES_DIR = (
+    find_resources() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
+_DASHBOARD_DIR_ENV = os.environ.get("EXO_DASHBOARD_DIR", None)
+DASHBOARD_DIR = (
+    find_dashboard() if _RESOURCES_DIR_ENV is None else Path.home() / _RESOURCES_DIR_ENV
+)
 
 # Log files (data/logs or cache)
 EXO_LOG = EXO_CACHE_HOME / "exo.log"

src/exo/shared/models/model_cards.py

@@ -12,16 +12,42 @@ from pydantic import (
     BaseModel,
     Field,
     PositiveInt,
+    ValidationError,
     field_validator,
     model_validator,
 )
+from tomlkit.exceptions import TOMLKitError
 
-from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS
+from exo.shared.constants import EXO_ENABLE_IMAGE_MODELS, RESOURCES_DIR
 from exo.shared.types.common import ModelId
 from exo.shared.types.memory import Memory
 from exo.utils.pydantic_ext import CamelCaseModel
 
-_card_cache: dict[str, "ModelCard"] = {}
+# kinda ugly...
+# TODO: load search path from config.toml
+_csp = [Path(RESOURCES_DIR) / "inference_model_cards"]
+if EXO_ENABLE_IMAGE_MODELS:
+    _csp.append(Path(RESOURCES_DIR) / "image_model_cards")
+
+CARD_SEARCH_PATH = _csp
+
+_card_cache: dict[ModelId, "ModelCard"] = {}
+
+
+async def _refresh_card_cache():
+    for path in CARD_SEARCH_PATH:
+        async for toml_file in path.rglob("*.toml"):
+            try:
+                card = await ModelCard.load_from_path(toml_file)
+                _card_cache[card.model_id] = card
+            except (ValidationError, TOMLKitError):
+                pass
+
+
+async def get_model_cards() -> list["ModelCard"]:
+    if len(_card_cache) == 0:
+        await _refresh_card_cache()
+    return list(_card_cache.values())
 
 
 class ModelTask(str, Enum):
@@ -55,28 +81,33 @@ class ModelCard(CamelCaseModel):
 
     async def save(self, path: Path) -> None:
         async with await open_file(path, "w") as f:
-            py = self.model_dump()
+            py = self.model_dump(exclude_none=True)
             data = tomlkit.dumps(py)  # pyright: ignore[reportUnknownMemberType]
             await f.write(data)
 
+    async def save_to_default_path(self):
+        await self.save(Path(RESOURCES_DIR) / (self.model_id.normalize() + ".toml"))
+
     @staticmethod
     async def load_from_path(path: Path) -> "ModelCard":
         async with await open_file(path, "r") as f:
             py = tomlkit.loads(await f.read())
             return ModelCard.model_validate(py)
 
+    # Is it okay that model card.load defaults to network access if the card doesn't exist? do we want to be more explicit here?
     @staticmethod
     async def load(model_id: ModelId) -> "ModelCard":
-        for card in MODEL_CARDS.values():
-            if card.model_id == model_id:
-                return card
-        return await ModelCard.from_hf(model_id)
-
-    @staticmethod
-    async def from_hf(model_id: ModelId) -> "ModelCard":
-        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        if model_id not in _card_cache:
+            await _refresh_card_cache()
         if (mc := _card_cache.get(model_id)) is not None:
             return mc
+
+        return await ModelCard.fetch_from_hf(model_id)
+
+    @staticmethod
+    async def fetch_from_hf(model_id: ModelId) -> "ModelCard":
+        """Fetches storage size and number of layers for a Hugging Face model, returns Pydantic ModelMeta."""
+        # TODO: failure if files do not exist
         config_data = await get_config_data(model_id)
         num_layers = config_data.layer_count
         mem_size_bytes = await get_safetensors_size(model_id)
@@ -89,544 +120,13 @@ class ModelCard(CamelCaseModel):
             supports_tensor=config_data.supports_tensor,
             tasks=[ModelTask.TextGeneration],
         )
+        await mc.save_to_default_path()
         _card_cache[model_id] = mc
         return mc
 
 
-MODEL_CARDS: dict[str, ModelCard] = {
-    # deepseek v3
-    "deepseek-v3.1-4bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-4bit"),
-        storage_size=Memory.from_gb(378),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "deepseek-v3.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/DeepSeek-V3.1-8bit"),
-        storage_size=Memory.from_gb(713),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # kimi k2
-    "kimi-k2-instruct-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Instruct-4bit"),
-        storage_size=Memory.from_gb(578),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2-thinking": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2-Thinking"),
-        storage_size=Memory.from_gb(658),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "kimi-k2.5": ModelCard(
-        model_id=ModelId("mlx-community/Kimi-K2.5"),
-        storage_size=Memory.from_gb(617),
-        n_layers=61,
-        hidden_size=7168,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.1
-    "llama-3.1-8b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"),
-        storage_size=Memory.from_mb(4423),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-8bit"),
-        storage_size=Memory.from_mb(8540),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-8b-bf16": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16"),
-        storage_size=Memory.from_mb(16100),
-        n_layers=32,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.1-70b": ModelCard(
-        model_id=ModelId("mlx-community/Meta-Llama-3.1-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.2
-    "llama-3.2-1b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-1B-Instruct-4bit"),
-        storage_size=Memory.from_mb(696),
-        n_layers=16,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(1777),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.2-3b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.2-3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(3339),
-        n_layers=28,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # llama-3.3
-    "llama-3.3-70b": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-4bit"),
-        storage_size=Memory.from_mb(38769),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Llama-3.3-70B-Instruct-8bit"),
-        storage_size=Memory.from_mb(73242),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "llama-3.3-70b-fp16": ModelCard(
-        model_id=ModelId("mlx-community/llama-3.3-70b-instruct-fp16"),
-        storage_size=Memory.from_mb(137695),
-        n_layers=80,
-        hidden_size=8192,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # qwen3
-    "qwen3-0.6b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-4bit"),
-        storage_size=Memory.from_mb(327),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-0.6b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-0.6B-8bit"),
-        storage_size=Memory.from_mb(666),
-        n_layers=28,
-        hidden_size=1024,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-4bit"),
-        storage_size=Memory.from_mb(16797),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-30b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-30B-A3B-8bit"),
-        storage_size=Memory.from_mb(31738),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-4bit"),
-        storage_size=Memory.from_mb(44800),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Instruct-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-4bit"),
-        storage_size=Memory.from_mb(44900),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-80b-a3B-thinking-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Next-80B-A3B-Thinking-8bit"),
-        storage_size=Memory.from_mb(84700),
-        n_layers=48,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-4bit"),
-        storage_size=Memory.from_gb(132),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-235b-a22b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-235B-A22B-Instruct-2507-8bit"),
-        storage_size=Memory.from_gb(250),
-        n_layers=94,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-4bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-4bit"),
-        storage_size=Memory.from_gb(270),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "qwen3-coder-480b-a35b-8bit": ModelCard(
-        model_id=ModelId("mlx-community/Qwen3-Coder-480B-A35B-Instruct-8bit"),
-        storage_size=Memory.from_gb(540),
-        n_layers=62,
-        hidden_size=6144,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # gpt-oss
-    "gpt-oss-120b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-120b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(68_996_301),
-        n_layers=36,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "gpt-oss-20b-MXFP4-Q8": ModelCard(
-        model_id=ModelId("mlx-community/gpt-oss-20b-MXFP4-Q8"),
-        storage_size=Memory.from_kb(11_744_051),
-        n_layers=24,
-        hidden_size=2880,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.5
-    "glm-4.5-air-8bit": ModelCard(
-        # Needs to be quantized g32 or g16 to work with tensor parallel
-        model_id=ModelId("mlx-community/GLM-4.5-Air-8bit"),
-        storage_size=Memory.from_gb(114),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=False,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.5-air-bf16": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.5-Air-bf16"),
-        storage_size=Memory.from_gb(214),
-        n_layers=46,
-        hidden_size=4096,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7
-    "glm-4.7-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-4bit"),
-        storage_size=Memory.from_bytes(198556925568),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-6bit"),
-        storage_size=Memory.from_bytes(286737579648),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-8bit-gs32": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-8bit-gs32"),
-        storage_size=Memory.from_bytes(396963397248),
-        n_layers=91,
-        hidden_size=5120,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # glm 4.7 flash
-    "glm-4.7-flash-4bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-4bit"),
-        storage_size=Memory.from_gb(18),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-5bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-5bit"),
-        storage_size=Memory.from_gb(21),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-6bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-6bit"),
-        storage_size=Memory.from_gb(25),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "glm-4.7-flash-8bit": ModelCard(
-        model_id=ModelId("mlx-community/GLM-4.7-Flash-8bit"),
-        storage_size=Memory.from_gb(32),
-        n_layers=47,
-        hidden_size=2048,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    # minimax-m2
-    "minimax-m2.1-8bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-8bit"),
-        storage_size=Memory.from_bytes(242986745856),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-    "minimax-m2.1-3bit": ModelCard(
-        model_id=ModelId("mlx-community/MiniMax-M2.1-3bit"),
-        storage_size=Memory.from_bytes(100086644736),
-        n_layers=61,
-        hidden_size=3072,
-        supports_tensor=True,
-        tasks=[ModelTask.TextGeneration],
-    ),
-}
-
-_IMAGE_BASE_MODEL_CARDS: dict[str, ModelCard] = {
-    "flux1-schnell": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-schnell"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23782357120),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-dev"),
-        storage_size=Memory.from_bytes(23782357120 + 9524621312),
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "flux1-krea-dev": ModelCard(
-        model_id=ModelId("exolabs/FLUX.1-Krea-dev"),
-        storage_size=Memory.from_bytes(23802816640 + 9524621312),  # Same as dev
-        n_layers=57,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_kb(0),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="text_encoder_2",
-                component_path="text_encoder_2/",
-                storage_size=Memory.from_bytes(9524621312),
-                n_layers=24,
-                can_shard=False,
-                safetensors_index_filename="model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(23802816640),
-                n_layers=57,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.TextToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-    "qwen-image-edit-2509": ModelCard(
-        model_id=ModelId("exolabs/Qwen-Image-Edit-2509"),
-        storage_size=Memory.from_bytes(16584333312 + 40860802176),
-        n_layers=60,
-        hidden_size=1,
-        supports_tensor=False,
-        tasks=[ModelTask.ImageToImage],
-        components=[
-            ComponentInfo(
-                component_name="text_encoder",
-                component_path="text_encoder/",
-                storage_size=Memory.from_bytes(16584333312),
-                n_layers=12,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-            ComponentInfo(
-                component_name="transformer",
-                component_path="transformer/",
-                storage_size=Memory.from_bytes(40860802176),
-                n_layers=60,
-                can_shard=True,
-                safetensors_index_filename="diffusion_pytorch_model.safetensors.index.json",
-            ),
-            ComponentInfo(
-                component_name="vae",
-                component_path="vae/",
-                storage_size=Memory.from_kb(0),
-                n_layers=None,
-                can_shard=False,
-                safetensors_index_filename=None,
-            ),
-        ],
-    ),
-}
-
-
-def _generate_image_model_quant_variants(
+# TODO: quantizing and dynamically creating model cards
+def _generate_image_model_quant_variants(  # pyright: ignore[reportUnusedFunction]
     base_name: str,
     base_card: ModelCard,
 ) -> dict[str, ModelCard]:
@@ -706,15 +206,6 @@ def _generate_image_model_quant_variants(
     return variants
 
 
-_image_model_cards: dict[str, ModelCard] = {}
-for _base_name, _base_card in _IMAGE_BASE_MODEL_CARDS.items():
-    _image_model_cards |= _generate_image_model_quant_variants(_base_name, _base_card)
-_IMAGE_MODEL_CARDS = _image_model_cards
-
-if EXO_ENABLE_IMAGE_MODELS:
-    MODEL_CARDS.update(_IMAGE_MODEL_CARDS)
-
-
 class ConfigData(BaseModel):
     model_config = {"extra": "ignore"}  # Allow unknown fields
 
@@ -742,6 +233,7 @@ class ConfigData(BaseModel):
             ["MiniMaxM2ForCausalLM"],
             ["LlamaForCausalLM"],
             ["GptOssForCausalLM"],
+            ["Step3p5ForCausalLM"],
         ]
 
     @model_validator(mode="before")

src/exo/utils/dashboard_path.py

@@ -1,31 +1,45 @@
-import os
 import sys
 from pathlib import Path
 from typing import cast
 
 
+def find_resources() -> Path:
+    resources = _find_resources_in_repo() or _find_resources_in_bundle()
+    if resources is None:
+        raise FileNotFoundError(
+            "Unable to locate resources. Did you clone the repo properly?"
+        )
+    return resources
+
+
+def _find_resources_in_repo() -> Path | None:
+    current_module = Path(__file__).resolve()
+    for parent in current_module.parents:
+        build = parent / "resources"
+        if build.is_dir():
+            return build
+    return None
+
+
+def _find_resources_in_bundle() -> Path | None:
+    frozen_root = cast(str | None, getattr(sys, "_MEIPASS", None))
+    if frozen_root is None:
+        return None
+    candidate = Path(frozen_root) / "resources"
+    if candidate.is_dir():
+        return candidate
+    return None
+
+
 def find_dashboard() -> Path:
-    dashboard = (
-        _find_dashboard_in_env()
-        or _find_dashboard_in_repo()
-        or _find_dashboard_in_bundle()
-    )
+    dashboard = _find_dashboard_in_repo() or _find_dashboard_in_bundle()
     if not dashboard:
         raise FileNotFoundError(
-            "Unable to locate dashboard assets - make sure the dashboard has been built, or export DASHBOARD_DIR if you've built the dashboard elsewhere."
+            "Unable to locate dashboard assets - you probably forgot to run `cd dashboard && npm install && npm run build && cd ..`"
         )
     return dashboard
 
 
-def _find_dashboard_in_env() -> Path | None:
-    env = os.environ.get("DASHBOARD_DIR")
-    if not env:
-        return None
-    resolved_env = Path(env).expanduser().resolve()
-
-    return resolved_env
-
-
 def _find_dashboard_in_repo() -> Path | None:
     current_module = Path(__file__).resolve()
     for parent in current_module.parents:

src/exo/utils/keyed_backoff.py

@@ -1,8 +1,10 @@
 import time
-from collections.abc import Hashable
+from typing import Generic, TypeVar
+
+K = TypeVar("K")
 
 
-class KeyedBackoff[K: Hashable]:
+class KeyedBackoff(Generic[K]):
     """Tracks exponential backoff state per key."""
 
     def __init__(self, base: float = 0.5, cap: float = 10.0):

src/exo/worker/engines/mlx/auto_parallel.py

@@ -31,6 +31,8 @@ from mlx_lm.models.qwen3_moe import Model as Qwen3MoeModel
 from mlx_lm.models.qwen3_moe import Qwen3MoeSparseMoeBlock
 from mlx_lm.models.qwen3_next import Model as Qwen3NextModel
 from mlx_lm.models.qwen3_next import Qwen3NextSparseMoeBlock
+from mlx_lm.models.step3p5 import Model as Step3p5Model
+from mlx_lm.models.step3p5 import Step3p5MLP
 
 from exo.shared.logging import logger
 from exo.shared.types.worker.shards import PipelineShardMetadata
@@ -380,6 +382,14 @@ def tensor_auto_parallel(
             all_to_sharded_linear_in_place,
             sharded_to_all_linear_in_place,
         )
+    elif isinstance(model, Step3p5Model):
+        tensor_parallel_sharding_strategy = Step3p5ShardingStrategy(
+            group,
+            all_to_sharded_linear,
+            sharded_to_all_linear,
+            all_to_sharded_linear_in_place,
+            sharded_to_all_linear_in_place,
+        )
     elif isinstance(model, GptOssModel):
         tensor_parallel_sharding_strategy = GptOssShardingStrategy(
             group,
@@ -774,3 +784,57 @@ class ShardedGptOssMoE(CustomMlxLayer):
         if self.sharding_group is not None:
             y = mx.distributed.all_sum(y, group=self.sharding_group)
         return y
+
+
+class Step3p5ShardingStrategy(TensorParallelShardingStrategy):
+    def shard_model(
+        self,
+        model: nn.Module,
+        timeout_seconds: float,
+        on_timeout: TimeoutCallback | None,
+    ) -> nn.Module:
+        model = cast(Step3p5Model, model)
+        for layer in model.layers:
+            eval_with_timeout(
+                layer.parameters(), timeout_seconds / len(model.layers), on_timeout
+            )
+            # Shard attention
+            layer.self_attn.q_proj = self.all_to_sharded_linear(layer.self_attn.q_proj)
+            layer.self_attn.k_proj = self.all_to_sharded_linear(layer.self_attn.k_proj)
+            layer.self_attn.v_proj = self.all_to_sharded_linear(layer.self_attn.v_proj)
+            layer.self_attn.o_proj = self.sharded_to_all_linear(layer.self_attn.o_proj)
+            layer.self_attn.num_heads //= self.N  # pyright: ignore[reportUnknownMemberType]
+            layer.self_attn.num_kv_heads //= self.N  # pyright: ignore[reportUnknownMemberType]
+
+            if isinstance(layer.mlp, Step3p5MLP):
+                # Dense MLP layer
+                layer.mlp.gate_proj = self.all_to_sharded_linear(layer.mlp.gate_proj)
+                layer.mlp.down_proj = self.sharded_to_all_linear(layer.mlp.down_proj)
+                layer.mlp.up_proj = self.all_to_sharded_linear(layer.mlp.up_proj)
+            else:
+                # MoE layer: shared expert + routed experts
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.gate_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.share_expert.down_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.share_expert.up_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.gate_proj)
+                self.sharded_to_all_linear_in_place(layer.mlp.switch_mlp.down_proj)
+                self.all_to_sharded_linear_in_place(layer.mlp.switch_mlp.up_proj)
+                layer.mlp = ShardedStep3p5MoE(layer.mlp)  # pyright: ignore[reportAttributeAccessIssue, reportArgumentType]
+                layer.mlp.sharding_group = self.group  # pyright: ignore[reportAttributeAccessIssue]
+
+            mx.eval(layer)
+        return model
+
+
+class ShardedStep3p5MoE(CustomMlxLayer):
+    def __init__(self, layer: _LayerCallable):
+        super().__init__(layer)
+        self.sharding_group: mx.distributed.Group | None = None
+
+    def __call__(self, x: mx.array) -> mx.array:
+        if self.sharding_group is not None:
+            x = sum_gradients(self.sharding_group)(x)
+        y = self.original_layer.__call__(x)
+        if self.sharding_group is not None:
+            y = mx.distributed.all_sum(y, group=self.sharding_group)
+        return y

src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py

@@ -16,7 +16,7 @@ from exo.download.download_utils import (
     ensure_models_dir,
     fetch_file_list_with_cache,
 )
-from exo.shared.models.model_cards import MODEL_CARDS, ModelCard, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId, get_model_cards
 from exo.worker.engines.mlx.utils_mlx import (
     get_eos_token_ids_for_model,
     load_tokenizer_for_model_id,
@@ -76,7 +76,7 @@ def get_test_models() -> list[ModelCard]:
     """Get a representative sample of models to test."""
     # Pick one model from each family to test
     families: dict[str, ModelCard] = {}
-    for card in MODEL_CARDS.values():
+    for card in asyncio.run(get_model_cards()):
         # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
         parts = card.model_id.short().split("-")
         family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
@@ -296,7 +296,7 @@ async def test_tokenizer_special_tokens(model_card: ModelCard) -> None:
 async def test_kimi_tokenizer_specifically():
     """Test Kimi tokenizer with its specific patches and quirks."""
     kimi_models = [
-        card for card in MODEL_CARDS.values() if "kimi" in card.model_id.lower()
+        card for card in await get_model_cards() if "kimi" in card.model_id.lower()
     ]
 
     if not kimi_models:
@@ -343,7 +343,7 @@ async def test_kimi_tokenizer_specifically():
 async def test_glm_tokenizer_specifically():
     """Test GLM tokenizer with its specific EOS tokens."""
     glm_model_cards = [
-        card for card in MODEL_CARDS.values() if "glm" in card.model_id.lower()
+        card for card in await get_model_cards() if "glm" in card.model_id.lower()
     ]
 
     if not glm_model_cards:

tests/headless_runner.py

@@ -10,7 +10,7 @@ from loguru import logger
 from pydantic import BaseModel
 
 from exo.shared.constants import EXO_MODELS_DIR
-from exo.shared.models.model_cards import MODEL_CARDS, ModelId
+from exo.shared.models.model_cards import ModelCard, ModelId
 from exo.shared.types.chunks import TokenChunk
 from exo.shared.types.commands import CommandId
 from exo.shared.types.common import Host, NodeId
@@ -114,13 +114,13 @@ async def run_test(test: Tests):
 
         instances: list[Instance] = []
         if test.kind in ["ring", "both"]:
-            i = ring_instance(test, hn)
+            i = await ring_instance(test, hn)
             if i is None:
                 yield "no model found"
                 return
             instances.append(i)
-        if test.kind in ["rdma", "both"]:
-            i = jaccl_instance(test)
+        if test.kind in ["jaccl", "both"]:
+            i = await jaccl_instance(test)
             if i is None:
                 yield "no model found"
                 return
@@ -145,7 +145,7 @@ async def run_test(test: Tests):
     return StreamingResponse(run())
 
 
-def ring_instance(test: Tests, hn: str) -> Instance | None:
+async def ring_instance(test: Tests, hn: str) -> Instance | None:
     hbn = [Host(ip="198.51.100.0", port=52417) for _ in test.devs]
     world_size = len(test.devs)
     for i in range(world_size):
@@ -158,11 +158,7 @@ def ring_instance(test: Tests, hn: str) -> Instance | None:
     else:
         raise ValueError(f"{hn} not in {test.devs}")
 
-    card = next(
-        (card for card in MODEL_CARDS.values() if card.model_id == test.model_id), None
-    )
-    if card is None:
-        return None
+    card = await ModelCard.load(test.model_id)
     instance = MlxRingInstance(
         instance_id=iid,
         ephemeral_port=52417,
@@ -230,12 +226,8 @@ async def execute_test(test: Tests, instance: Instance, hn: str) -> list[Event]:
     return []
 
 
-def jaccl_instance(test: Tests) -> MlxJacclInstance | None:
-    card = next(
-        (card for card in MODEL_CARDS.values() if card.model_id == test.model_id), None
-    )
-    if card is None:
-        return None
+async def jaccl_instance(test: Tests) -> MlxJacclInstance | None:
+    card = await ModelCard.load(test.model_id)
     world_size = len(test.devs)
     assert test.ibv_devs
 

uv.lock

@@ -366,6 +366,7 @@ version = "0.3.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiofiles", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "aiohttp", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "anyio", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "exo-pyo3-bindings", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "fastapi", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -386,6 +387,7 @@ dependencies = [
     { name = "rustworkx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "tiktoken", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "tomlkit", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "types-aiofiles", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
 ]
 
 [package.dev-dependencies]
@@ -401,6 +403,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "aiofiles", specifier = ">=24.1.0" },
+    { name = "aiohttp", specifier = ">=3.12.14" },
     { name = "anyio", specifier = "==4.11.0" },
     { name = "exo-pyo3-bindings", editable = "rust/exo_pyo3_bindings" },
     { name = "fastapi", specifier = ">=0.116.1" },
@@ -421,6 +424,7 @@ requires-dist = [
     { name = "rustworkx", specifier = ">=0.17.1" },
     { name = "tiktoken", specifier = ">=0.12.0" },
     { name = "tomlkit", specifier = ">=0.14.0" },
+    { name = "types-aiofiles", specifier = ">=24.1.0.20250708" },
 ]
 
 [package.metadata.requires-dev]
@@ -1068,8 +1072,8 @@ wheels = [
 
 [[package]]
 name = "mlx-lm"
-version = "0.30.5"
-source = { git = "https://github.com/ml-explore/mlx-lm?branch=main#96699e6dadb13b82b28285bb131a0741997d19ae" }
+version = "0.30.6"
+source = { git = "https://github.com/ml-explore/mlx-lm?branch=main#ab050d1fac2ef1d7bea6b8d870f1e5717d7f59f5" }
 dependencies = [
     { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
     { name = "mlx", marker = "sys_platform == 'darwin'" },