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vLLM v0.31.0 enhances serving with FlashMLA, DeepGEMM, and MXFP8

By PulseAugur Editorial · [1 sources] ·

vLLM has released version 0.31.0, bringing significant enhancements to its serving capabilities. This update integrates FlashMLA with V4.1 NVFP4 compressed KV cache as the default for SM100, alongside the inclusion of DeepGEMM. The release also features sparse MQA logits, Mega-Gate fusing, and fused small-batch WO-A with MXFP8 quantization. AI

IMPACT vLLM's latest release offers performance improvements for AI serving infrastructure.

RANK_REASON This is a software release for an infrastructure tool, not a frontier model release or significant industry event.

Read on Mastodon — sigmoid.social →

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

vLLM v0.31.0 enhances serving with FlashMLA, DeepGEMM, and MXFP8

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3 / 100
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Tool
This is a software release for an infrastructure tool, not a frontier model release or significant industry event.
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    vllm v0.31.0 introduces major serving upgrades, making FlashMLA mega attention with V4.1 NVFP4 compressed KV cache the SM100 default. Release includes DeepGEMM

    vllm v0.31.0 introduces major serving upgrades, making FlashMLA mega attention with V4.1 NVFP4 compressed KV cache the SM100 default. Release includes DeepGEMM sparse MQA logits, Mega-Gate fusing, and fused small-batch WO-A with MXFP8 quant. # AI # MachineLearning # DevOps

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