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MII makes low-latency and high-throughput inference possible, powered by DeepSpeed. The Deep Learning (DL) open-source community has seen tremendous growth in the last few months. Incredibly powerful text generation models such as the Bloom 176B, or image generation model such as Stable Diffusion are now available to anyone with access to a handful or even a single GPU through platforms such as Hugging Face. While open-sourcing has democratized access to AI capabilities, their application is still restricted by two critical factors: inference latency and cost. DeepSpeed-MII is a new open-source python library from DeepSpeed, aimed towards making low-latency, low-cost inference of powerful models not only feasible but also easily accessible. MII offers access to the highly optimized implementation of thousands of widely used DL models. MII-supported models achieve significantly lower latency and cost compared to their original implementation.
Features
- MII offers access to highly optimized implementation of thousands of widely used DL models
- MII supported models achieve significantly lower latency and cost compared to their original implementation
- MII leverages an extensive set of optimizations from DeepSpeed-Inference such as deepfusion for transformers
- State-of-the-art performance
- MII supports low-cost deployment of these models both on-premises and on Azure via AML with just a few lines of codes
- Automated tensor-slicing for multi-GPU inference, on-the-fly quantization with ZeroQuant
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