Custom Quantization Boosts R9700 by 5.8%
alphatrad · reddit · 2026-07-18
The author tuned a custom quantization format Q80ROCMFPX for three Radeon AI PRO R9700 cards, aiming to outperform generic Q8 on gfx1201.
Results
- Model size is 2.94% smaller than upstream Q80
- HumanEval: 140/164, tying with the original
- Full model decoding: 5.77%–5.87% faster
- Equal workload test: Median 5.82% faster, winning 17/18 paired groups, p=0.000965
- End-to-end agent total runtime: Only 2.21% faster, missing the author's 3% threshold
Constraints
- Currently cannot run directly on upstream llama.cpp, Ollama, LM Studio, or vLLM
- Requires the author's specific ROCmFPX fork and source compilation
- This optimization is primarily effective for gfx1201 / R9700, offering limited benefits for NVIDIA or other AMD cards
The author provided a full experimental repository and noted it would be helpful if more people reproduced the results and compared them against stock Q8.
More from Infra
- LLM Serving Metrics Thread: Why TPOT and Uptime Make or Break User Experience — abhijithneil · 2026-09-11
- PlanetScale launches sharded Postgres: 768 servers acting as one, 1PB scale — dhruv2038 · 2026-09-11
- Can a 7900 XTX 24GB run Qwen locally? Reddit seeks ROCm tok/s benchmarks — thenomadexplorerlife · 2026-09-11
- RTK Terminal Compression Cuts Tokens but Leaves Your AI Coding Bill Unchanged — Bartaseth · 2026-09-11
- SF Compute founder: buying compute is 'an absolutely awful experience' right now — IgorCarron · 2026-09-11
- SmolVM open-sources persistent computer infrastructure for agents that outlive chat sessions — aniketmaurya · 2026-09-11