# microsoft/Phi-4-multimodal-instruct — vLLM serving recipe

> Microsoft Phi-4 multimodal model supporting text, image, and audio inputs with text outputs.

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

- Interactive page: https://recipes.vllm.ai/microsoft/Phi-4-multimodal-instruct
- This configuration as JSON: https://recipes.vllm.ai/microsoft/Phi-4-multimodal-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`, `multi_node_tp` (multi-node strategies also need `nodes=<n>`)

## Install

Requires vLLM >= 0.8.0.

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

## Serve

```bash
vllm serve microsoft/Phi-4-multimodal-instruct \
  --trust-remote-code \
  --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 microsoft/Phi-4-multimodal-instruct \
  --trust-remote-code \
  --tensor-parallel-size 1
```

## Guide

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

### Overview

Phi-4-multimodal-instruct processes text, image, and audio inputs and
generates text. `--trust-remote-code` is retained because the model's
multimodal LoRA components require it.

### Prerequisites

- Hardware: Intel Xeon 6/Xeon 5 CPUs
- vLLM >= 0.8.0

#### Docker (Intel Xeon 6 CPUs)

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

### Intel Xeon 6

```bash
vllm serve microsoft/Phi-4-multimodal-instruct \
  --trust-remote-code \
  --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 microsoft/Phi-4-multimodal-instruct \
  --trust-remote-code \
  --tensor-parallel-size 1
```

The example uses TP=1. Adjust TP/DP for the deployment topology;
host-specific CPU placement is intentionally not hard-coded.

### 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/microsoft/Phi-4-multimodal-instruct)
- [vLLM Phi-4 usage guide](https://docs.vllm.ai/projects/recipes/en/stable/Microsoft/Phi-4.html)
- [vLLM CPU installation](https://docs.vllm.ai/en/latest/getting_started/installation/cpu/)