Technical Implementation of DeepSeek LLM Deployment with Expert Parallelism on 96 H100 GPUs
By
GabrielBianconi
Master baker tier. Every paragraph earns its place on the tray.
Summary
The article details the technical implementation of deploying DeepSeek, an open-source large language model, across 96 H100 GPUs using advanced parallel processing techniques. It explains how the system employs prefill-decode disaggregation and large-scale expert parallelism to achieve high-performance inference at scale, specifically achieving 52.3k input processing speed on 12 nodes in Atlas Cloud infrastructure.
Key quotes
· 4 pulledDeepSeek is a popular open-source large language model (LLM) praised for its strong performance
Its large size and unique architecture, which uses Multi-head Latent Attention (MLA) and Mixture of Experts (MoE), require an advanced system for efficient serving at scale
Our implementation runs on 12 nodes in the Atlas Cloud, each equipped with 8 H100 GPUs
It uses prefill-decode disaggregation and large-scale expert parallelism (EP), achieving a speed of 52.3k input
DeepSeek is a popular open-source large language model (LLM) praised for its strong performance. However, its large size and unique architecture, which us...
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