Introduction
DeepSpeed-MII (Model Implementations for Inference) is an open-source library from DeepSpeed designed to enable low-latency, high-throughput inference for large models. It applies techniques such as blocked KV-caching, continuous batching, Dynamic SplitFuse, tensor parallelism, and high-performance CUDA kernels to maximize throughput and reduce latency.
Key Features
- High-throughput text generation with optimizations like blocked KV caching and continuous batching.
- Dynamic SplitFuse and specialized CUDA kernels for improved efficiency.
- Support for multi-GPU tensor parallelism, model replicas, and RESTful API serving.
- Wide model compatibility via Hugging Face integration and many supported model families.
Use Cases
- Production model serving where throughput and latency are critical.
- Research and benchmarking of inference optimizations and kernels.
- Deploying persistent or non-persistent inference pipelines across GPUs and clusters.
Technical Highlights
- Blocked KV-caching and continuous batching to improve memory and throughput efficiency.
- Tensor parallelism and model replica support for scalable multi-GPU deployments.
- RESTful API gateway for easy integration with external services.