IntermediatePython

vllm-mlxMLX Inference Server Optimized for Apple Silicon

vllm-mlx is a native MLX inference server designed for Apple Silicon, offering OpenAI and Anthropic API compatibility. It supports text and vision-language models with continuous batching. The vendor claims over 400 tokens per second on M1 Ultra, making it suitable for local development and privacy-sensitive deployments. The project is primarily written in Python and licensed under Apache-2.0.

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Project Overview

vllm-mlx is a native MLX inference server designed for Apple Silicon, offering OpenAI and Anthropic API compatibility. It supports text and vision-language models with continuous batching. The vendor claims over 400 tokens per second on M1 Ultra, making it suitable for local development and privacy-sensitive deployments. The project is primarily written in Python and licensed under Apache-2.0.

Project Overview

vllm-mlx is a native MLX inference server optimized for Apple Silicon hardware, providing compatibility with OpenAI and Anthropic APIs for seamless integration into existing applications.

Key Features

  • Supports text and vision-language models
  • Continuous batching for efficient processing
  • API compatibility with OpenAI and Anthropic specifications

Performance

According to the README, it achieves over 400 tokens per second on M1 Ultra, ideal for local development and privacy-sensitive scenarios.

Tech Stack and License

The primary language is Python, and the project is licensed under Apache-2.0.

Getting Started

For installation and usage instructions, please refer to the project README.

vllm-mlxApple SiliconMLXLLM inferencevision-language modellocal inferencecontinuous batchingMCP tool callingOpenAI compatibleAnthropic compatible

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Frequently Asked Questions

What is vllm-mlx: MLX Inference Server Optimized for Apple Silicon?

vllm-mlx is a native MLX inference server designed for Apple Silicon, offering OpenAI and Anthropic API compatibility. It supports text and vision-language models with continuous batching. The vendor claims over 400 tokens per second on M1 Ultra, making it suitable for local development and privacy-sensitive deployments. The project is primarily written in Python and licensed under Apache-2.0.

What language is vllm-mlx: MLX Inference Server Optimized for Apple Silicon written in?

vllm-mlx: MLX Inference Server Optimized for Apple Silicon is primarily written in Python.

What license is vllm-mlx: MLX Inference Server Optimized for Apple Silicon under?

vllm-mlx: MLX Inference Server Optimized for Apple Silicon is released under the Apache-2.0 license.

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