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llama.cpp optimizes RDNA3.5 GPU performance with new tiling strategy

By PulseAugur Editorial · [1 sources] ·

The llama.cpp project has released an update, b11618, which optimizes performance for RDNA3.5 GPUs. This update introduces 64-row MMQ tiles for specific quant types, aiming to improve processing speeds on hardware like gfx115x and gfx1151. The optimizations were tested using MUL_MAT and MUL_MAT_ID operations, showing median performance gains of up to 8.0%. The development was assisted by Claude Opus-5. AI

IMPACT Improves inference performance on specific AMD GPUs, potentially benefiting users running local LLMs.

RANK_REASON This is a software optimization for a specific hardware architecture within an open-source project, not a frontier model release or significant industry event.

Read on llama.cpp — Releases →

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

llama.cpp optimizes RDNA3.5 GPU performance with new tiling strategy

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3 / 100
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Tool
This is a software optimization for a specific hardware architecture within an open-source project, not a frontier model release or significant industry event.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, model release
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High
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Same-day
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COVERAGE [1]

  1. llama.cpp — Releases TIER_1 English(EN) · liangliangchang ·

    b11618: HIP: use 64-row MMQ tiles for some quant types on RDNA3.5 (#28195)

    <p>The RDNA3.5 config table selects a 64-row / 128-thread tile up to J=32 and a<br /> 128-row / 256-thread tile above that. On gfx115x the smaller tile leaves enough<br /> LDS free for a second workgroup per CU, while the larger one does not.</p> <p>Extend the 64-row tile to the …

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