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mudler's LocalAI [bot]andEttore Di Giacinto a0f50b2af2 feat(vllm-cpp): serve MiniMax-H3 video+audio generation (#11424) 
* feat(vllm-cpp): serve MiniMax-H3 video+audio generation

vllm.cpp's C ABI grew a video slice (ABI v12): a second engine handle
loaded from the MiniMax-H3 checkpoint SET, one blocking generate, and a
composed ffmpeg argv the caller execs. This wires that into LocalAI's
existing /video endpoint, so `vllm-cpp` now serves both text and video
and a clip comes back as an MP4 with a real audio track rather than a
silent render.

The video engine is a separate handle rather than a mode of the text
one because H3 is not a model directory: the DiT, the text encoder and
two VAEs are separate artifacts, and vllm.cpp has the two loaders refuse
each other's checkpoints. `Load` takes the video branch when the config
declares any of the video options; `parameters.model` is the DiT and the
rest of the set is named in `options:`.

Three details are worth calling out because getting them wrong is
expensive:

- The partition is DECLARED, not detected. The community quantisations
  strip the release metadata and the FL2VA and Ref2VA DiTs are
  byte-structurally identical, so the engine refuses to generate until
  it is told which it has. Worse, a mismatch does not fail cleanly: a
  reference passed to an FL2VA DiT renders for hours and returns a
  coloured lattice over the frame. The backend refuses that combination
  up front instead.
- ffmpeg comes from the host. libvllm writes frames plus a WAV and
  composes the mux argv, then spawns nothing - that process boundary is
  upstream's decision. The backend execs it, the same arrangement
  vibevoice-cpp uses for transcoding, and ffmpeg also converts a
  start_image upload into the binary PPM at the exact output canvas the
  engine requires.
- It is slow. Roughly 176 s per denoise step at the default 1344x768
  canvas on a 20-SM device, so the 50-step default is a multi-hour job.
  Nothing on this path imposes a deadline.

The /video endpoint no longer forces 512x512 when the request omits the
geometry. Every video backend already supplies its own default for a
zero (512x512 for stablediffusion-ggml, 1280x720 for diffusers, 832x480
for longcat-video, 1344x768 for H3), so the hardcoded value only ever
overrode the model's trained canvas with one three of the four were
never trained at.

Moving the engine pin from ABI v10 to v16 also grows the text
vllm_model_params mirror by the v14 device field and the v16 KV-sizing
knobs. LocalAI sets none of them - 0 is the pre-v14 engine byte for byte
- but the struct SIZE is part of the layout contract, so leaving them
out would have vllm_engine_load read past the allocation.

Gallery: `minimax-h3-fl2va-q4` installs the Q4_K_M FL2VA set (~40 GB
across five weight files plus the two VAE configs that carry the latent
statistics).

Assisted-by: Claude:claude-opus-5 golangci-lint yamllint go-vet

* fix(vllm-cpp): unbreak the Darwin build at the new engine pin

src/capi/vllm_c.cpp opens one `extern "C" {` for the whole ABI surface,
so file-local helpers declared inside it inherit C linkage. The video
slice added one that returns std::string, which Apple Clang reports as
-Wreturn-type-c-linkage and vllm.cpp's target-local -Werror turns into a
build failure. GCC and upstream Clang do not diagnose it, so only the
metal-darwin-arm64 job saw it.

Suppress it the same way this Makefile already suppresses Apple Clang's
-Wgnu-folding-constant on the Metal build. The helper is never called
across the boundary so the warning describes no hazard here, but it is a
real upstream wart: the fix belongs in vllm.cpp, hoisting the helper
above the extern "C" block, and this flag should go when a pin carrying
that fix lands.

Assisted-by: Claude:claude-opus-5

* fix(vllm-cpp): patch the engine clone instead of the warning flag

The -Wno-return-type-c-linkage added in the previous commit does nothing.
vllm_cpp_set_warnings adds `-Wall -Wextra -Werror` as PRIVATE target
options, so they land after anything CMAKE_CXX_FLAGS contributes, and
-Wall re-enables the -Wreturn-type group that -Wreturn-type-c-linkage
belongs to. The darwin job failed again on the same line, which is the
evidence: a consumer cannot wave this off from outside the engine.

Position is the only fix, so carry it as a patch against the pinned SHA,
the way longcat-video patches its own upstream. It hoists the helper
above the `extern "C" {` that gives it C linkage; it is file-local and
never called across the boundary, so nothing else moves.

`git apply` is unguarded on purpose: a patch that stops applying must
fail the clone loudly, because the alternative is a pin that silently
ships without a fix it is documented to carry. The patch header names
what retires it - a pin carrying the fix upstream, where it belongs.

Verified by applying the patch with `git apply` to the exact blob at the
pinned SHA and diffing the result against the intended file.

Assisted-by: Claude:claude-opus-5

* chore(vllm-cpp): bump the engine pin to ABI v17 and drop the vendored OrEmpty patch

The OrEmpty linkage fix this backend carried as patches/0001-* landed upstream
(mudler/vllm.cpp#195, 7534da65), so the patch has done its job. It is deleted
rather than left in place: the Makefile applies patches/*.patch unguarded and
documents that "a patch that no longer applies must FAIL the clone", so keeping
it against fixed source would break the build the moment the pin moved. Bumping
the pin and deleting the patch therefore have to be the SAME change.

Pin f921062b -> 776c56f1 (current vllm.cpp main).

That range also carries the engine's ABI v17 (vllm_server_main: the OpenAI server
published on the public surface). registerLib compares the library's
vllm_abi_version against `abiVersion` for EXACT equality, so the constant moves
16 -> 17 in the same commit or every load fails with an ABI mismatch.

The bump is safe for the layout assertions in video_test.go: diffing include/vllm.h
across the two pins shows zero struct-field changes -- v17 adds one function
declaration, the version macro and a doc comment, nothing else -- so every
unsafe.Offsetof in the video params test still holds.

Assisted-by: Claude Code:claude-opus-5 [ClaudeCode]

* chore(vllm-cpp): re-pin to pick up the VLLM_CPP_SERVER=OFF link fix

The previous pin carried vllm.cpp's ABI v17 (vllm_server_main) but not the guard
that makes it link when the server is compiled out. This backend builds libvllm
with VLLM_CPP_SERVER off, so the darwin lane failed at the dylib link with
vllm::entrypoints::openai::VllmServerMain undefined.

Fixed upstream in mudler/vllm.cpp#202: the C entry point is now guarded, so the
symbol is still exported (ABI v17 stays resolvable for dlopen) while the
no-server arm reports the missing capability instead of dragging in a translation
unit that was never compiled.

Verified upstream in BOTH arms before re-pinning: SERVER=ON builds and runs, and
SERVER=OFF configures, links, produces libvllm.so, and `nm -D` shows
vllm_server_main exported next to vllm_video_generate and vllm_transcribe.

Assisted-by: Claude Code:claude-opus-5 [ClaudeCode]

---------

Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-08-09 22:34:51 +02:00

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mudler%2FLocalAI | Trendshift

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LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.

A small core, not a bundle. Each backend wraps a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX...) in its own image, pulled only when a model needs it. You install nothing you don't use.

  • Composable by design: backends are separate and pulled on demand, so you install only what your model needs
  • Open and extensible: load any model, or build your own backend in any language against an open interface
  • Drop-in API compatibility: OpenAI, Anthropic, and ElevenLabs APIs across every backend
  • Any model, any modality: LLMs, vision, voice, image, and video behind one API
  • Any hardware: NVIDIA, AMD, Intel, Apple Silicon, Vulkan, or CPU-only
  • Multi-user ready: API key auth, user quotas, role-based access
  • Built-in AI agents: autonomous agents with tool use, RAG, MCP, and skills
  • Privacy-first: your data never leaves your infrastructure

A small LocalAI core with backends (llama.cpp, vLLM, MLX, whisper.cpp, stable-diffusion, kokoro, parakeet.cpp...) plugged in as separate on-demand images

Created by Ettore Di Giacinto and maintained by the LocalAI team.

📖 Documentation | 💬 Discord | 💻 Quickstart | 🖼️ Models | ❓FAQ

Guided tour

https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18

Click to see more!

User and auth

https://github.com/user-attachments/assets/228fa9ad-81a3-4d43-bfb9-31557e14a36c

Agents

https://github.com/user-attachments/assets/6270b331-e21d-4087-a540-6290006b381a

Usage metrics per user

https://github.com/user-attachments/assets/cbb03379-23b4-4e3d-bd26-d152f057007f

Fine-tuning and Quantization

https://github.com/user-attachments/assets/5ba4ace9-d3df-4795-b7d4-b0b404ea71ee

WebRTC

https://github.com/user-attachments/assets/ed88e34c-fed3-4b83-8a67-4716a9feeb7b

Quickstart

macOS

Download LocalAI for macOS

Note: The DMG is not signed by Apple. After installing, run: sudo xattr -d com.apple.quarantine /Applications/LocalAI.app. See #6268 for details.

Containers (Docker, podman, ...)

Already ran LocalAI before? Use docker start -i local-ai to restart an existing container.

CPU only:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest

NVIDIA GPU:

# CUDA 13
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13

# CUDA 12
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12

# NVIDIA Jetson ARM64 (CUDA 12, for AGX Orin and similar)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64

# NVIDIA Jetson ARM64 (CUDA 13, for DGX Spark)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13

AMD GPU (ROCm):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas

Intel GPU (oneAPI):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel

Vulkan GPU:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan

Loading models

# From the model gallery (see available models with `local-ai models list` or at https://models.localai.io)
local-ai run llama-3.2-1b-instruct:q4_k_m
# From Huggingface
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
# From the Ollama OCI registry
local-ai run ollama://gemma:2b
# From a YAML config
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
# From a standard OCI registry (e.g., Docker Hub)
local-ai run oci://localai/phi-2:latest

To work with a running LocalAI server from the terminal, start the built-in agent from another shell. It answers questions, reads your files and runs commands on your machine, asking you to approve anything that changes state. Inside a session, /models lists installed models and /model <name> switches between them. See the Terminal agent docs.

# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m

# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m

Automatic Backend Detection: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see GPU Acceleration.

For more details, see the Getting Started guide.

Latest News

For older news and full release notes, see GitHub Releases and the blog.

Features

Supported Backends & Acceleration

LocalAI supports 60+ backends including llama.cpp, vLLM, SGLang, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for NVIDIA (CUDA 12/13), AMD (ROCm), Intel (oneAPI/SYCL), Apple Silicon (Metal), Vulkan, and NVIDIA Jetson (L4T). All backends can be installed on-the-fly from the Backend Gallery.

See the full Backend & Model Compatibility Table and GPU Acceleration guide.

Backends built by us

Most backends wrap a best-in-class upstream engine. A handful of them are native C/C++/GGML engines (no Python at inference) developed and maintained by the LocalAI project itself:

Backend What it does
vllm.cpp From-scratch C++20 port of vLLM for text generation: paged KV cache, continuous batching, prefix caching, safetensors + GGUF loading, engine-enforced structured output, on CPU, CUDA, Metal and Vulkan. Also serves MiniMax-H3 joint video+audio generation
parakeet.cpp C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription
moss-transcribe.cpp C++/GGML port of OpenMOSS MOSS-Transcribe-Diarize: joint long-form transcription, speaker diarization and timestamping in a single pass
moss-tts.cpp C++/GGML port of the OpenMOSS MOSS-TTS family: text-to-speech (MOSS-TTS-Local v1.5, 48 kHz stereo) with reference-audio voice cloning, through the MOSS-Audio-Tokenizer neural codec
magpie-tts.cpp C++/GGML port of NVIDIA's Magpie TTS Multilingual 357M: 22.05 kHz mono text-to-speech in 5 voices and 9+ languages, with the NanoCodec neural codec and tokenizer/G2P embedded in a single GGUF
ced.cpp C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition
voice-detect.cpp Speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion), replacing the Python speaker-recognition backend
voxtral-tts.c Mistral Voxtral-4B-TTS text-to-speech in pure C: 20 preset voices across 9 languages, 24 kHz WAV output, no dependencies beyond libc
vibevoice.cpp Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization
rf-detr.cpp Native RF-DETR object detection and instance segmentation
locate-anything.cpp Open-vocabulary object detection and visual grounding (LocateAnything-3B)
depth-anything.cpp Depth Anything 3 monocular metric depth + camera pose estimation
face-detect.cpp Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend
free-splatter.cpp Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required
trellis2.cpp C++/GGML port of Microsoft TRELLIS.2: single-image to textured 3D mesh (GLB with PBR materials)
privacy-filter.cpp Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier
LocalVQE Joint acoustic echo cancellation, noise suppression, and dereverberation
local-store Local-first vector database for embeddings (shipped in-tree)

We also maintain apex-quant, a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.

Resources

Team

LocalAI is maintained by a small team of humans, together with the wider community of contributors.

A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in Discord — LocalAI is a community-driven project and wouldn't exist without you. See the full contributors list.

Citation

If you utilize this repository, data in a downstream project, please consider citing it with:

@misc{localai,
  author = {Ettore Di Giacinto},
  title = {LocalAI: The free, Open source OpenAI alternative},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/go-skynet/LocalAI}},

Sponsors

Do you find LocalAI useful?

Support the project by becoming a backer or sponsor. Your logo will show up here with a link to your website.

A huge thank you to our generous sponsors who support this project covering CI expenses, and our Sponsor list:

Past sponsors


Individual sponsors

A special thanks to individual sponsors, a full list is on GitHub and buymeacoffee. Special shout out to drikster80 for being generous. Thank you everyone!

Star history

LocalAI Star history Chart

License

LocalAI is a community-driven project created by Ettore Di Giacinto and maintained by the LocalAI team.

MIT - Author Ettore Di Giacinto mudler@localai.io

Acknowledgements

LocalAI couldn't have been built without the help of great software already available from the community. Thank you!

Contributors

This is a community project, a special thanks to our contributors!

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