DeepSpeed-MII is a Python library designed to enable high-throughput, low-latency, and cost-effective inference for large language models and text-to-image models. It targets researchers and developers needing to deploy models efficiently, offering significant performance gains over existing solutions.

How It Works

MII leverages DeepSpeed-Inference and incorporates key technologies like blocked KV-caching, continuous batching, Dynamic SplitFuse, and tensor parallelism. This combination automatically optimizes models based on architecture, size, batch size, and hardware, minimizing latency and maximizing throughput.

Quick Start & Requirements

• Install via pip: pip install deepspeed-mii
• Requires NVIDIA GPUs with compute capability 8.0+ (Ampere+), CUDA 11.6+, and Ubuntu 20+.
• Pre-compiled wheels are provided via the deepspeed-kernels library.
• See Getting Started with MII for examples.

Highlighted Details

• Supports over 37,000 models across 11 popular architectures (e.g., Llama, Mistral, Mixtral, Falcon, Qwen).
• Achieves up to 2.5x higher effective throughput compared to vLLM.
• Offers both non-persistent (script-based) and persistent (gRPC server) deployment options.
• Includes support for RESTful API endpoints for inference.

Maintenance & Community

• Developed by DeepSpeed (Microsoft).
• Contributions welcome under the Developer Certificate of Origin (DCO).
• Follows the Microsoft Open Source Code of Conduct.

Licensing & Compatibility

• MIT License.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

• While supporting a vast number of models, specific architecture support should be verified.
• Performance claims are benchmark-dependent and may vary with specific hardware and model configurations.