# Qwen/Qwen3-VL-30B-A3B-Instruct — vLLM serving recipe

> Qwen3-VL MoE vision-language model with 30B total / 3B active parameters, supporting image, video, and text workloads.

Configuration: hardware `xeon6` (4× Xeon 6) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (BF16) · features `encoder_parallel`

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/Qwen/Qwen3-VL-30B-A3B-Instruct
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/Qwen/Qwen3-VL-30B-A3B-Instruct.json?hardware=xeon6

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `xeon6`
- `variant`: `default`
- `strategy`: `single_node_tp`, `single_node_tep`, `single_node_dep`, `multi_node_tp`, `multi_node_tp_pp`, `multi_node_dep`, `multi_node_tep` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `text_only`, `encoder_parallel` (default: `encoder_parallel`)

## Install

Requires vLLM >= 0.11.0.

Docker image: `vllm/vllm-openai-cpu:latest-x86_64`

Extra dependencies:

- Recommended for offline multimodal inference (image/video pre-processing helpers) (optional)

```bash
uv pip install qwen-vl-utils==0.0.14
```

## Serve

```bash
vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
  --tensor-parallel-size 1 \
  --mm-encoder-tp-mode data
```

Docker equivalent:

```bash
docker run --shm-size=16g \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai-cpu:latest-x86_64 Qwen/Qwen3-VL-30B-A3B-Instruct \
  --tensor-parallel-size 1 \
  --mm-encoder-tp-mode data
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

Qwen3-VL-30B-A3B-Instruct is a multimodal Qwen3-VL MoE model. Intel Xeon 6
CPU serving was validated with BF16.

### Prerequisites

- Hardware: Intel Xeon 6 CPUs
- vLLM >= 0.11.0

#### pip (Intel Xeon 6 CPUs)

For Intel and AMD x86 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

```bash
vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
  --tensor-parallel-size 1 \
  --mm-encoder-tp-mode data
```

Docker (the image entrypoint is `vllm serve`):

```bash
docker run \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai-cpu:latest-x86_64 Qwen/Qwen3-VL-30B-A3B-Instruct \
  --tensor-parallel-size 1 \
  --mm-encoder-tp-mode data
```

The example uses TP=1. Adjust TP/DP based on system topology; NUMA binding is
intentionally not hard-coded in the recipe.

### Configuration Notes

- TP/DP values and topology-specific CPU binding remain deployment choices.

### 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-VL-30B-A3B-Instruct)
- [vLLM CPU installation](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/)