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DSv4-Flash optimization boosts LLM inference speed on NVIDIA GH200

A new optimization technique called DSv4-Flash has been developed to significantly speed up large language model inference on NVIDIA GH200 hardware. This optimization, when implemented with vLLM and SGLang, can achieve over 300 tokens per second for text generation and support context lengths of up to 1 million tokens within 192 GB of HBM. The improvements are detailed in a write-up that includes specific configuration steps and performance metrics. AI

IMPACT Enhances LLM inference performance, potentially enabling larger context windows and faster generation on high-end hardware.

RANK_REASON The item details a new optimization technique for LLM inference on specific hardware, including performance benchmarks and implementation details. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DSv4-Flash optimization boosts LLM inference speed on NVIDIA GH200

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The item details a new optimization technique for LLM inference on specific hardware, including performance benchmarks and implementation details. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. r/LocalLLaMA TIER_1 English(EN) · /u/Reddactor ·

    Optimised DSv4-Flash for 2x GH200: 10,000 tok/s PP, >300 tok/s TG on SGLang

    <!-- SC_OFF --><div class="md"><p>There are some PRs to use and a nice trick to speed up PP on really longs contexts in my write up. Hope it helps!</p> <p><strong><em>TL;DR:</em></strong><br /> <code>On this dual GH200 box, you build vLLM v0.26.0 from source, add the merged DSV4 …