chore(deps): bump torch in /backend/python/vllm

Bumps torch from 2.9.1+cpu to 2.12.1+xpu.

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.12.1+xpu
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

backend/cpp/llama-cpp/grpc-server.cpp

@@ -613,6 +613,24 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
     // starts with '-'. Applied once after the loop via common_params_parse.
     std::vector<std::string> extra_argv;
 
+    auto add_device_options = [&](const std::string & devices) {
+        const std::regex regex{ R"([,]+)" };
+        std::sregex_token_iterator it{ devices.begin(), devices.end(), regex, -1 };
+        std::vector<std::string> split_arg{ it, {} };
+
+        for (std::string device : split_arg) {
+            const auto start = device.find_first_not_of(" \t\n\r");
+            if (start == std::string::npos) {
+                continue;
+            }
+            const auto end = device.find_last_not_of(" \t\n\r");
+            device = device.substr(start, end - start + 1);
+
+            extra_argv.push_back("--device");
+            extra_argv.push_back(device);
+        }
+    };
+
      // decode options. Options are in form optname:optvale, or if booleans only optname.
     for (int i = 0; i < request->options_size(); i++) {
         std::string opt = request->options(i);
@@ -744,6 +762,10 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
             } else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
                 params.no_op_offload = false;
             }
+        } else if (!strcmp(optname, "device") || !strcmp(optname, "devices")) {
+            if (optval != NULL) {
+                add_device_options(optval_str);
+            }
         } else if (!strcmp(optname, "split_mode") || !strcmp(optname, "sm")) {
             // Accepts: none | layer | row | tensor (the latter requires a llama.cpp build
             // that includes ggml-org/llama.cpp#19378, FlashAttention enabled, and KV-cache

backend/python/vllm/requirements-cpu.txt

@@ -1,6 +1,6 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
 accelerate
-torch==2.9.1+cpu
+torch==2.12.1+xpu
 torchvision
 torchaudio
 transformers

backend/python/vllm/requirements-l4t13-after.txt

@@ -1,4 +1,12 @@
 # vLLM 0.20+ ships an aarch64 manylinux wheel on PyPI whose Requires-Dist pins
 # torch==2.11.0 / torchvision==0.26.0 / torchaudio==2.11.0, locking an ABI-
 # consistent set with the cu130 torch wheel installed above.
-vllm
+#
+# Pinned to match the cublas13 build (requirements-cublas13-after.txt) and to
+# stay deterministic on GB10 / DGX Spark: 0.24.0 carries vllm-project/vllm#45179
+# ("release cached device memory under pressure on UMA GPUs during weight
+# loading"), without which cold model loads on the Grace Blackwell unified-
+# memory architecture crash deterministically with an empty "Engine core init
+# failed" set (mudler/LocalAI#10722). Leaving this unpinned let the L4T image
+# drift onto whatever wheel was latest at build time.
+vllm==0.24.0

docs/content/advanced/model-configuration.md

@@ -493,6 +493,7 @@ These llama.cpp options are passed through the `options:` array.
 | `threads_batch` / `n_threads_batch` | int | same as `threads` | Threads used during prompt processing. `<= 0` means `hardware_concurrency()`. |
 | `direct_io` / `use_direct_io` | bool | `false` | Open the model with `O_DIRECT` (faster cold loads on NVMe; ignored if not supported). |
 | `verbosity` | int | `3` | llama.cpp internal log verbosity threshold. Higher = more verbose. |
+| `device` / `devices` | string | all devices | Select the llama.cpp backend devices to use. Repeat the option or pass a comma-separated list; unlisted devices are excluded. Use the names reported by `llama-server --list-devices` / `--list-devices`. |
 | `override_tensor` / `tensor_buft_overrides` | string | "" | Per-tensor buffer-type overrides for the main model. Format: `<tensor regex>=<buffer type>,<tensor regex>=<buffer type>,...`. Mirrors the existing `draft_override_tensor` syntax for the draft model. |
 | `cpu_moe` | bool | false | Keep all MoE expert weights of the main model on CPU (upstream `--cpu-moe`). Frees VRAM on large MoE models (DeepSeek, Qwen3 `*-A3B`). |
 | `n_cpu_moe` | int | 0 | Keep MoE expert weights of the first N main-model layers on CPU (upstream `--n-cpu-moe`). |
@@ -514,6 +515,7 @@ options:
   - "--cpu-moe"                 # boolean flag
   - "--n-cpu-moe:4"             # flag with a value
   - "--override-tensor:exps=CPU"
+  - "devices:CUDA1,CUDA2,CUDA3" # skip CUDA0, e.g. a display GPU
 ```
 
 Notes:

docs/content/features/text-generation.md

@@ -512,6 +512,7 @@ The `llama.cpp` backend supports additional configuration options that can be sp
 | `check_tensors` | boolean | Validate tensor data for invalid values during model loading. Default: `false`. | `check_tensors:true` |
 | `warmup` | boolean | Enable warmup run after model loading. Default: `true`. | `warmup:false` |
 | `no_op_offload` | boolean | Disable offloading host tensor operations to device. Default: `false`. | `no_op_offload:true` |
+| `device` or `devices` | string | Select the llama.cpp backend devices to use. Repeat the option or pass a comma-separated list; unlisted devices are excluded. Use the names reported by `llama-server --list-devices` / `--list-devices`. | `devices:CUDA1,CUDA2,CUDA3` |
 | `kv_unified` or `unified_kv` | boolean | Use a single unified KV buffer shared across all sequences. Default: `true` (LocalAI override; upstream defaults to `false` but auto-enables it when slot count is auto). **Required for `cache_idle_slots` to work**: without it the server force-disables idle-slot saving at init, and the prompt cache is never written across requests. | `kv_unified:false` |
 | `cache_idle_slots` or `idle_slots_cache` | boolean | On a new task, save the previous slot's KV state into the prompt cache (and clear the slot) so a later request with the same prefix can warm-load it. Default: `true`. Auto-disabled by the server if `kv_unified=false` or `cache_ram=0`. | `cache_idle_slots:false` |
 | `n_ctx_checkpoints` or `ctx_checkpoints` | integer | Maximum number of context checkpoints per slot (used for partial-prefix recovery, e.g. SWA). Default: `32`. | `ctx_checkpoints:16` |
@@ -530,6 +531,7 @@ options:
   - context_shift:true
   - cache_ram:4096
   - parallel:2
+  - devices:CUDA1,CUDA2,CUDA3
   - fit_params:true
   - fit_target:1024
   - slot_prompt_similarity:0.5