vLLM/recipes/models/Qwen/Qwen3-14B.yaml

meta:
  title: "Qwen3-14B"
  slug: "qwen3-14b"
  provider: "Qwen"
  description: "Qwen3 14B dense model with hybrid thinking/non-thinking modes, validated for Intel Xeon 6 CPU serving."
  date_added: 2026-08-17
  date_updated: 2026-09-12
  tasks:
    - text
  performance_headline: "Validated on Intel Xeon 6 CPU with BF16 serving"
  related_recipes:
    - "Qwen/Qwen3-4B"
    - "Qwen/Qwen3-32B"
  hardware:
    xeon6: verified

model:
  model_id: "Qwen/Qwen3-14B"
  min_vllm_version: "0.8.5"
  architecture: dense
  parameter_count: "14B"
  active_parameters: "14B"
  context_length: 40960
  base_args: []
  base_env: {}

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","factor":4.0,"original_max_position_embeddings":32768}}'
      - "--max-model-len"
      - "131072"

opt_in_features:
  - reasoning
  - long_context

variants:
  default:
    precision: bf16
    vram_minimum_gb: 34
    description: "Full precision BF16"
    supported_hardware:
      - xeon6
  fp8:
    model_id: "Qwen/Qwen3-14B-FP8"
    precision: fp8
    vram_minimum_gb: 17
    description: "Qwen official FP8 checkpoint"
  awq:
    model_id: "Qwen/Qwen3-14B-AWQ"
    precision: int4
    vram_minimum_gb: 10
    description: "Qwen official AWQ 4-bit checkpoint"
    supported_hardware:
      - xeon6
    extra_args:
      - "--quantization"
      - "awq"

compatible_strategies:
  - single_node_tp
  - multi_node_tp

kv_offload_support:
  offloading_cpu: verified
  offloading_fs: verified

hardware_overrides: {}

strategy_overrides: {}

guide: |
  ## Overview

  Qwen3-14B is a Qwen3 dense model with hybrid thinking and non-thinking modes.
  Intel Xeon 6 CPU serving was validated with BF16.

  ## Prerequisites

  - Hardware: Intel Xeon 6 CPUs
  - vLLM >= 0.8.5

  ### pip (Intel Xeon 6 CPUs)

  Follow the [CPU pre-built wheels](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/#pre-built-wheels)
  installation instructions.

  ### Docker (Intel Xeon 6 CPUs)

  ```bash
  docker pull vllm/vllm-openai-cpu:latest-x86_64
  ```

  ## Intel Xeon 6

  Choose tensor parallelism based on the system topology; the recipe does not
  prescribe a fixed TP/DP layout or hard-code NUMA node IDs.

  ```bash
  vllm serve Qwen/Qwen3-14B
  ```

  Docker:

  ```bash
  docker run \
    --privileged --ipc=host -p 8000:8000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    vllm/vllm-openai-cpu:latest-x86_64 Qwen/Qwen3-14B \
    --tensor-parallel-size 1 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes
  ```

  ## Configuration Notes

  - TP/DP values and explicit CPU bindings are intentionally not prescribed.
  - The `qwen3` reasoning parser is available in vLLM 0.9.0 and later.

  ## Runtime and Platform Tuning

  The following settings are intentionally not prescribed as portable CPU model
  defaults because they depend on the workload, hardware, or runtime environment:

  - `--max-num-batched-tokens`: scheduler/throughput tuning.
  - `--max-num-seqs`: concurrency and scheduler-capacity tuning.
  - `--gpu-memory-utilization`: platform memory-budget tuning.
  - `--no-enable-prefix-caching`: workload/benchmark cache-policy tuning.
  - `VLLM_ENGINE_ITERATION_TIMEOUT_S`: operational runtime timeout.

  These settings may still appear in validated hardware-specific overrides. Tune
  them at deployment time based on platform resources, workload shape, and
  latency/throughput goals.

  ## References

  - [Model card](https://huggingface.co/Qwen/Qwen3-14B)
  - [vLLM CPU installation](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/)