Vllm Mlx
Description
High-performance OpenAI and Anthropic compatible LLM inference server for Apple Silicon. Native MLX, continuous batching, multimodal models, MCP tool calling, and Claude Code support.
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
vllm-mlx
**Continuous batching + OpenAI + Anthropic APIs in one server. Native Apple Silicon inference.**
**Read this in other languages:** [English](README.md) · [Español](README.es.md) · [Français](README.fr.md) · [中文](README.zh.md)
[](https://pypi.org/project/vllm-mlx/) [](https://pepy.tech/projects/vllm-mlx) [](https://www.python.org/downloads/) [](LICENSE) [](https://support.apple.com/en-us/HT211814) [](https://github.com/waybarrios/vllm-mlx)
What is vllm-mlx?
A vLLM-style inference server for Apple Silicon Macs. Unlike `Ollama` or `mlx-lm` used directly, it ships **continuous batching, paged KV cache, prefix caching, and SSD-tiered cache**, and exposes **both OpenAI `/v1/*` and Anthropic `/v1/messages`** from a single process. Run LLMs, vision models, audio, and embeddings on Metal with unified memory, no conversion step.
Quick start (30 seconds)
pip install vllm-mlx
vllm-mlx serve mlx-community/Llama-3.2-3B-Instruct-4bit --port 8000 --continuous-batching
**OpenAI SDK:**
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
r = client.chat.completions.create(model="default", messages=[{"role": "user", "content": "Hi!"}])
print(r.choices[0].message.content)
**Anthropic SDK / Claude Code:**
export ANTHROPIC_BASE_URL=http://localhost:8000
export ANTHROPIC_API_KEY=not-needed
claude
Features
APIs
- OpenAI-compatible:
/v1/chat/completions,/v1/completions,/v1/embeddings,/v1/rerank,/v1/responses - Anthropic-compatible: `/v1/mes
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