# openai/whisper-large-v3 — vLLM serving recipe

> Whisper Large v3 speech recognition model, validated for Intel Xeon 6 CPU serving.

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

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/openai/whisper-large-v3
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/openai/whisper-large-v3.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`, `multi_node_tp` (multi-node strategies also need `nodes=<n>`)

## Install

Requires vLLM >= 0.7.0.

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

## Serve

```bash
vllm serve openai/whisper-large-v3 \
  --tensor-parallel-size 1
```

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 openai/whisper-large-v3 \
  --tensor-parallel-size 1
```

## Guide

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

### Overview

Whisper Large v3 is OpenAI's speech recognition model. vLLM exposes
transcription through the OpenAI-compatible audio transcription endpoint.

### Prerequisites

- Hardware: Intel Xeon 6/Xeon 5 CPUs
- vLLM >= 0.7.0

#### Docker (Intel Xeon 6 CPUs)

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

### Intel Xeon 6

```bash
vllm serve openai/whisper-large-v3 \
  --tensor-parallel-size 1
```

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 openai/whisper-large-v3 \
  --tensor-parallel-size 1
```

The example uses TP=1. Adjust TP/DP for the deployment topology.

### 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/openai/whisper-large-v3)
- [vLLM Whisper implementation](https://docs.vllm.ai/en/latest/api/vllm/model_executor/models/whisper/)
- [vLLM CPU installation](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/)