🦙Reddit r/LocalLLaMA•Mar 27, 2026Stalecollected in 2h

RX 9070 ROCm Beats Vulkan with Flash Attention

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🦙Read original on Reddit r/LocalLLaMA

#rdna4 #rocm-benchmarks #flash-attnrocm-7.2.1-on-rx-9070rx-9070 rocm-7.2.1 flashattention llama.cpp

💡Flash attn turns RX 9070 ROCm into PP beast—5.5x speedup revealed

⚡ 30-Second TL;DR

What Changed

ROCm + flash attn: 3,980 t/s PP512 on Qwen3-8B (5.5x over default)

Why It Matters

Unlocks RDNA4 potential for local LLM inference via proper ROCm flags, closing gap with Vulkan. Early benchmarks guide AMD GPU optimization for practitioners.

What To Do Next

Build llama.cpp with -DGGML_CUDA_FORCE_MMQ=ON -DGGML_HIP_GRAPHS=ON and run --flash-attn on ROCm 7.2.1.

Who should care:Developers & AI Engineers

Key Points

•ROCm + flash attn: 3,980 t/s PP512 on Qwen3-8B (5.5x over default)
•Qwen3.5-14B-A3B: ROCm 3,731 t/s PP512 (+12% vs Vulkan), Vulkan 113 t/s TG
•Advantage shrinks at 8K context; parity there
•Build flags: -DGGML_HIP=ON -DGGML_CUDA_FORCE_MMQ=ON -DGGML_HIP_GRAPHS=ON --flash-attn

🦙Read original article on Reddit r/LocalLLaMA

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