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Fix gfx1103 performance regression#21720

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matteoserva:fix-gfx1103
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Fix gfx1103 performance regression#21720
matteoserva wants to merge 2 commits intoggml-org:masterfrom
matteoserva:fix-gfx1103

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Overview

Fixes the performance regression on gfx1103 (AMD IGPU used on mini pc)

Solves #20647
caused by 617db24

Requirements

westers and others added 2 commits March 27, 2026 02:42
Commit 617db24 added nwarps=8 tuning for RDNA3_0 discrete GPUs
(W7900/gfx1100), which causes ~30% throughput regression on
gfx1103 iGPU (Radeon 780M/760M): 18 tok/s down to 13 tok/s.

The iGPU has significantly less memory bandwidth and fewer CUs
than discrete RDNA3 GPUs, so the higher warp count causes
thread contention rather than improving throughput.

Fix: detect gfx1103 specifically and use RDNA2 MMVQ parameters
(nwarps=1) instead of RDNA3_0 parameters (nwarps=8). This
restores pre-617db241 performance on iGPU while keeping the
optimization for discrete RDNA3 GPUs.

Changes:
- vendors/hip.h: Add RDNA3_IGPU define for __gfx1103__
- common.cuh: Add GGML_CUDA_CC_RDNA3_IGPU constant and macro
- mmvq.cu: Route gfx1103 to RDNA2 parameter table (nwarps=1)

Fixes ggml-org#20647

Signed-off-by: Steve Westers <westers@users.noreply.github.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
@matteoserva matteoserva requested review from a team and IMbackK as code owners April 10, 2026 11:43
@github-actions github-actions bot added Nvidia GPU Issues specific to Nvidia GPUs ggml changes relating to the ggml tensor library for machine learning labels Apr 10, 2026
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