{
  "hf_id": "Qwen/Qwen3-30B-A3B",
  "meta": {
    "title": "Qwen3-30B-A3B",
    "slug": "qwen3-30b-a3b",
    "provider": "Qwen",
    "description": "Qwen3 MoE model with 30.5B total and 3.3B active parameters, validated for BF16 serving on Intel Xeon 6 and Intel Arc Pro B70 and W8A8 serving on Ascend A3.",
    "date_added": "2026-08-14",
    "date_updated": "2026-09-23",
    "tasks": [
      "text"
    ],
    "performance_headline": "Validated on Intel Xeon 6 (GNR), Ascend A3 W8A8 and 4x Intel Arc Pro B70 with BF16 serving",
    "related_recipes": [
      "Qwen/Qwen3-235B-A22B-Instruct-2507"
    ],
    "hardware": {
      "xeon6": "verified",
      "ascend_910c": "verified",
      "arc_pro_b70": "verified"
    }
  },
  "recommended_command": {
    "hardware": "arc_pro_b70",
    "strategy": "single_node_tp",
    "variant": "default",
    "node_count": 1,
    "deploy_type": "single_node",
    "env": {},
    "docker_image": "vllm/vllm-openai-xpu:latest",
    "command": "vllm serve Qwen/Qwen3-30B-A3B \\\n  --tensor-parallel-size 4 \\\n  --block-size 64 \\\n  --enforce-eager \\\n  --no-enable-prefix-caching \\\n  --disable-sliding-window \\\n  --max-model-len 9472 \\\n  --max-num-batched-tokens 8192 \\\n  --gpu-memory-utilization 0.9 \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser hermes \\\n  --reasoning-parser qwen3",
    "argv": [
      "vllm",
      "serve",
      "Qwen/Qwen3-30B-A3B",
      "--tensor-parallel-size",
      "4",
      "--block-size",
      "64",
      "--enforce-eager",
      "--no-enable-prefix-caching",
      "--disable-sliding-window",
      "--max-model-len",
      "9472",
      "--max-num-batched-tokens",
      "8192",
      "--gpu-memory-utilization",
      "0.9",
      "--enable-auto-tool-choice",
      "--tool-call-parser",
      "hermes",
      "--reasoning-parser",
      "qwen3"
    ],
    "docker_command": "docker run --device /dev/dri \\\n  -v /dev/dri/by-path:/dev/dri/by-path --shm-size=16g \\\n  --privileged --ipc=host -p 8000:8000 \\\n  -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai-xpu:latest Qwen/Qwen3-30B-A3B \\\n  --tensor-parallel-size 4 \\\n  --block-size 64 \\\n  --enforce-eager \\\n  --no-enable-prefix-caching \\\n  --disable-sliding-window \\\n  --max-model-len 9472 \\\n  --max-num-batched-tokens 8192 \\\n  --gpu-memory-utilization 0.9 \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser hermes \\\n  --reasoning-parser qwen3",
    "docker_argv": [
      "docker",
      "run",
      "--device",
      "/dev/dri",
      "-v",
      "/dev/dri/by-path:/dev/dri/by-path",
      "--shm-size",
      "16g",
      "--privileged",
      "--ipc=host",
      "-p",
      "8000:8000",
      "-v",
      "~/.cache/huggingface:/root/.cache/huggingface",
      "vllm/vllm-openai-xpu:latest",
      "Qwen/Qwen3-30B-A3B",
      "--tensor-parallel-size",
      "4",
      "--block-size",
      "64",
      "--enforce-eager",
      "--no-enable-prefix-caching",
      "--disable-sliding-window",
      "--max-model-len",
      "9472",
      "--max-num-batched-tokens",
      "8192",
      "--gpu-memory-utilization",
      "0.9",
      "--enable-auto-tool-choice",
      "--tool-call-parser",
      "hermes",
      "--reasoning-parser",
      "qwen3"
    ],
    "strategy_spec": {
      "name": "single_node_tp",
      "deploy_type": "single_node",
      "display_name": "Tensor Parallel",
      "short_label": "TP",
      "orientation": "latency",
      "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n",
      "hardware_match": {
        "min_gpus": 1,
        "max_gpus": 8,
        "multi_node": false
      },
      "vllm_args": [],
      "parallel_flag": "--tensor-parallel-size"
    },
    "hardware_profile": {
      "brand": "Intel",
      "generation": "xpu",
      "display_name": "Arc Pro B70",
      "description": "Intel Arc Pro B70 32 GB · 4-card node · vLLM XPU backend",
      "gpu_count": 4,
      "vram_gb": 128,
      "multi_node": false,
      "restricted": true
    },
    "alternatives": {
      "multi_node_tp": "/Qwen/Qwen3-30B-A3B/strategies/multi_node_tp.json"
    },
    "by_hardware": {
      "arc_pro_b70": "/Qwen/Qwen3-30B-A3B/hw/arc_pro_b70.json",
      "xeon6": "/Qwen/Qwen3-30B-A3B/hw/xeon6.json"
    }
  },
  "model": {
    "model_id": "Qwen/Qwen3-30B-A3B",
    "min_vllm_version": "0.8.5",
    "architecture": "moe",
    "parameter_count": "30.5B",
    "active_parameters": "3.3B",
    "context_length": 40960,
    "base_args": [],
    "base_env": {},
    "install": {
      "pip": {
        "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto"
      },
      "docker": {
        "command": "docker pull vllm/vllm-openai:latest"
      }
    }
  },
  "features": {
    "tool_calling": {
      "description": "Enable automatic tool choice with the Hermes parser",
      "args": [
        "--enable-auto-tool-choice",
        "--tool-call-parser",
        "hermes"
      ]
    },
    "reasoning": {
      "description": "Enable Qwen3 thinking mode with the Qwen3 reasoning parser",
      "args": [
        "--reasoning-parser",
        "qwen3"
      ]
    },
    "long_context": {
      "description": "YaRN RoPE scaling to the model card's validated 131,072-token context. Static YaRN holds the scaling factor constant regardless of input length, so leave this off unless you actually serve long prompts — it can cost quality below 32K.",
      "args": [
        "--hf-overrides",
        "{\"max_position_embeddings\":131072,\"rope_parameters\":{\"rope_type\":\"yarn\",\"rope_theta\":1000000,\"factor\":4.0,\"original_max_position_embeddings\":32768}}",
        "--max-model-len",
        "131072"
      ]
    }
  },
  "opt_in_features": [
    "long_context"
  ],
  "hardware_opt_in_features": {
    "ascend_910c": [
      "tool_calling",
      "reasoning"
    ]
  },
  "variants": {
    "default": {
      "precision": "bf16",
      "vram_minimum_gb": 74,
      "description": "Full precision BF16",
      "supported_hardware": [
        "xeon6",
        "arc_pro_b70"
      ]
    },
    "ascend_w8a8": {
      "model_id": "Eco-Tech/Qwen3-30B-A3B-w8a8",
      "precision": "int8",
      "label": "W8A8 (Ascend A3)",
      "vram_minimum_gb": 37,
      "description": "ModelSlim INT8 W8A8 checkpoint for Ascend 910C, verified with TP4 + expert parallelism.",
      "supported_hardware": [
        "ascend_910c"
      ],
      "tp": 4,
      "min_vllm_version": "0.23.0",
      "extra_args": [
        "--trust-remote-code",
        "--max-num-seqs",
        "100",
        "--max-model-len",
        "40960",
        "--max-num-batched-tokens",
        "16384",
        "--enable-expert-parallel",
        "--quantization",
        "ascend",
        "--distributed_executor_backend",
        "mp",
        "--no-enable-prefix-caching",
        "--async-scheduling",
        "--compilation-config",
        "{\"cudagraph_mode\": \"FULL_DECODE_ONLY\"}",
        "--additional-config",
        "{\"enable_flashcomm1\": true, \"weight_nz_mode\": 2}",
        "--gpu-memory-utilization",
        "0.95"
      ],
      "extra_env": {
        "VLLM_USE_MODELSCOPE": "True",
        "HCCL_OP_EXPANSION_MODE": "AIV",
        "HCCL_BUFFSIZE": "1024",
        "OMP_PROC_BIND": "false",
        "OMP_NUM_THREADS": "1",
        "TASK_QUEUE_ENABLE": "1",
        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
        "VLLM_USE_RUST_FRONTEND": "1"
      },
      "hardware_overrides": {
        "ascend_910c": {
          "docker_image": "swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/vllm-ascend:v0.23.0-a3"
        }
      },
      "json": "/Eco-Tech/Qwen3-30B-A3B-w8a8.json"
    }
  },
  "hardware_overrides": {
    "cpu": {
      "extra_args": [
        "--max-num-batched-tokens",
        "16384",
        "--gpu-memory-utilization",
        "0.8"
      ]
    },
    "xpu": {
      "extra_args": [
        "--block-size",
        "64",
        "--enforce-eager",
        "--no-enable-prefix-caching",
        "--disable-sliding-window",
        "--max-model-len",
        "9472",
        "--max-num-batched-tokens",
        "8192",
        "--gpu-memory-utilization",
        "0.9"
      ]
    }
  },
  "guide": "## Overview\n\nQwen3-30B-A3B is a mixture-of-experts Qwen3 model with 30.5B total parameters\nand 3.3B active parameters per token. This recipe includes an Intel Xeon 6 CPU\nconfiguration validated with BF16 serving and an Ascend A3 W8A8 variant.\n\n## Prerequisites\n\n- Hardware: 4x Xeon 6 CPUs, or 4x Intel Arc Pro B70 (32 GB per card).\n- vLLM >= 0.8.5\n\n### pip (Intel Xeon 6 CPUs)\n\nFor Intel and AMD x86 CPUs, follow the\n[CPU pre-built wheels](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/#pre-built-wheels)\ninstallation instructions.\n\n### Docker (Intel Xeon 6 CPUs)\n\n```bash\ndocker pull vllm/vllm-openai-cpu:latest-x86_64\n```\n\n### Docker (Intel Arc Pro B70)\n\n```bash\ndocker pull vllm/vllm-openai-xpu:latest\n```\n\n## Intel Xeon 6\n\nChoose the tensor parallel size based on the system topology and deployment\nrequirements. Add `--tensor-parallel-size <N>` when needed; the recipe does\nnot prescribe a Xeon 6 TP value or hard-code NUMA node IDs.\n\n```bash\nvllm serve Qwen/Qwen3-30B-A3B \\\n  --max-num-batched-tokens 16384 \\\n  --gpu-memory-utilization 0.8\n```\n\nDocker:\n\n```bash\ndocker run \\\n  --privileged --ipc=host -p 8000:8000 \\\n  -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai-cpu:latest-x86_64 Qwen/Qwen3-30B-A3B \\\n  --tensor-parallel-size 1 \\\n  --max-num-batched-tokens 16384 \\\n  --gpu-memory-utilization 0.8 \\\n  --enable-auto-tool-choice \\\n  --tool-call-parser hermes \\\n  --reasoning-parser qwen3\n```\n\n## Intel Arc Pro B70 (XPU)\n\nValidated on 4x Intel Arc Pro B70 (32 GB per card) with the official vLLM XPU\nimage `vllm/vllm-openai-xpu:latest`, BF16, TP=4.\n\n```bash\ndocker run --device /dev/dri \\\n  -v /dev/dri/by-path:/dev/dri/by-path --shm-size=16g \\\n  --privileged --ipc=host -p 8000:8000 \\\n  -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai-xpu:latest \\\n  Qwen/Qwen3-30B-A3B \\\n  --tensor-parallel-size 4 \\\n  --block-size 64 \\\n  --enforce-eager \\\n  --no-enable-prefix-caching \\\n  --disable-sliding-window \\\n  --max-model-len 9472 \\\n  --max-num-batched-tokens 8192 \\\n  --gpu-memory-utilization 0.9\n```\n\n## Client Usage\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"unused\")\nresponse = client.chat.completions.create(\n    model=\"Qwen/Qwen3-30B-A3B\",\n    messages=[{\"role\": \"user\", \"content\": \"Explain tensor parallelism briefly.\"}],\n)\nprint(response.choices[0].message.content)\n```\n\n## References\n\n- [Model card](https://huggingface.co/Qwen/Qwen3-30B-A3B)\n- [Ascend A3 W8A8 checkpoint](https://www.modelscope.cn/models/Eco-Tech/Qwen3-30B-A3B-w8a8)\n- [vLLM Ascend](https://github.com/vllm-project/vllm-ascend)\n- [vLLM CPU installation](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/)\n",
  "source": {
    "source": "vllm",
    "repo": "vllm-project/vllm",
    "ref": "main",
    "sha": "389ffebe555705c85337025d5df108d0384d365e",
    "path": "recipes/models/Qwen/Qwen3-30B-A3B.yaml",
    "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/Qwen/Qwen3-30B-A3B.yaml",
    "overlays": [
      {
        "source": "vllm-ascend",
        "repo": "vllm-project/vllm-ascend",
        "ref": "main",
        "sha": "02ac27a19cb7676304a9ca6985ba56ce29664536",
        "path": "recipes/overlays/Qwen/Qwen3-30B-A3B.yaml",
        "edit_url": "https://github.com/vllm-project/vllm-ascend/edit/main/recipes/overlays/Qwen/Qwen3-30B-A3B.yaml"
      }
    ]
  }
}