microsoft/Phi-4-multimodal-instruct

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)

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

Intel Xeon 6

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

Docker (the image entrypoint is vllm serve):

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
• vLLM Phi-4 usage guide
• vLLM CPU installation