<b> DeepSpeed trained the world's most powerful language models ([MT-530B](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/), [BLOOM](https://huggingface.co/blog/bloom-megatron-deepspeed)); [learn how](https://www.deepspeed.ai/tutorials/large-models-w-deepspeed/).</b>
*[2022/10] [DeepSpeed-MII: instant speedup on 24,000+ open-source DL models with up to 40x cheaper inference](https://www.deepspeed.ai/2022/10/10/mii.html)
*[2022/09] [ZeRO-Inference: Democratizing massive model inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html)
*[2022/07] [Azure and DeepSpeed empower easy-to-use and high-performance model training](https://azure.microsoft.com/en-us/blog/azure-empowers-easytouse-highperformance-and-hyperscale-model-training-using-deepspeed/)
*[2022/07] [DeepSpeed Compression: A composable library for extreme compression](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
@@ -11,7 +11,7 @@ The Deep Learning (DL) open-source community has seen tremendous growth in the l
There has been significant progress in system optimizations for DL model inference that can drastically reduce both latency and cost, but those are not easily accessible. The main reason for this limited accessibility is that the DL model inference landscape is diverse with models varying in size, architecture, system performance characteristics, hardware requirements, etc. Identifying the appropriate set of system optimizations applicable to a given model and applying them correctly is often beyond the scope of most data scientists, making low latency and low-cost inference mostly inaccessible.
[DeepSpeed-MII](https://github.com/microsoft/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.
[DeepSpeed Model Implementations for Inference (MII)](https://github.com/microsoft/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 highly optimized implementations of **thousands of widely used DL models.**
* MII supported models achieve significantly lower latency and cost compared to their original implementation.
<b> DeepSpeed trained the world's most powerful language models ([MT-530B](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/), [BLOOM](https://huggingface.co/blog/bloom-megatron-deepspeed)); [learn how](https://www.deepspeed.ai/tutorials/large-models-w-deepspeed/).</b>
*[2022/10] [DeepSpeed-MII: instant speedup on 24,000+ open-source DL models with up to 40x cheaper inference](https://www.deepspeed.ai/2022/10/10/mii.html)
*[2022/09] [ZeRO-Inference: Democratizing massive model inference](https://www.deepspeed.ai/2022/09/09/zero-inference.html)
*[2022/07] [Azure and DeepSpeed empower easy-to-use and high-performance model training](https://azure.microsoft.com/en-us/blog/azure-empowers-easytouse-highperformance-and-hyperscale-model-training-using-deepspeed/)
*[2022/07] [DeepSpeed Compression: A composable library for extreme compression](https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/)
