MI50 power curve tests

tests done power limiting the GPU on LACT - real power usage varies wildy at 20W it ranges from 25W to 56W same behavior happens on every setting prompt for the test runs: https://github.com/lukesdevlab/youtube/blob/main/prompts/agent-maze.txt analysis by mimo 2.5 Key Findings: • Generation speed is remarkably resilient to power throttling — 100W delivers 97.5% of 190W gen speed (31.98 vs 32.79 t/s), since decode is memory-bandwidth bound, not compute bound. • At 50W you get 70% of peak gen speed at only 26% of peak power — 3.6× better energy efficiency (0.458 vs 0.173 t/s/W). • At 20W the card is 6.0× more energy efficient than 190W, though prompt processing drops to 53% of peak. • Graph reuse correlates inversely with power — 190W reuses 44,790 graphs vs 11,669 at 100W, but 20W reuses 38,248. Lower power limits cause more partial graph reuse as the scheduler compensates for throttled compute. • Prompt processing degrades faster than gen under power limits — 190W→20W: prompt drops to 53% (691→366 t/s), gen drops to 63% (32.8→20.8 t/s). Prompt processing is more compute-bound than memory-bound. • For inference-heavy deployments, 50W is the optimal operating point on MI50 — near-peak gen speed with dramatically lower power draw and cooling requirements. Avarage of 3 runs: 190W config consistently processed a lot less total tokens than everyone else and didnt produce a working file in 1 out of 3 runs TDP Prompt Speed Gen Speed Total Time Total Tokens Gen t/s per Watt Graphs Reused Relative Perf 190W 691.28 t/s 32.79 t/s 212.4 s 14,892 0.173 t/s/W 44,790 100% 100W 603.08 t/s 31.98 t/s 244.9 s 21,529 0.320 t/s/W 11,669 97.5% 50W 401.14 t/s 22.92 t/s 315.1 s 20,861 0.458 t/s/W 31,967 70.0% 20W 366.05 t/s 20.80 t/s 319.9 s 20,295 1.040 t/s/W 38,248 63.4% llama.cpp parameters: [+] Model: qwen/Qwen3.6-35B-A3B-UD-IQ4_NL_XL.gguf [+] Context: 262144 (256K tokens) [+] Target KV: K=q8_0 / V=q8_0 [+] MoE placement: PARTIAL (21 MoE layers on CPU, rest on GPU) [+] MTP: OFF (non-MTP model) [+] Port: 8882 [+] Container: llama-gfx906-qwen35b-no-mtp [+] Parallel: 2 slot(s) [+] GPU layers: 99 [+] Threads: 6 / 6 (batch) [+] Batch/Ubatch: 2048 / 1024 [+] Ctx checkpoints: 0 hardware used: Ryzen 5 5600 2x16Gb DDR4 2667 MI50 16Gb software: harness used: pi.dev Arch Linux with Kernel 7.1.4-arch1-1 docker.io/mixa3607/llama.cpp-gfx906:b10087-rocm-6.3.3 submitted by /u/Atretador [link] [comments]

Source: r/LocalLLaMA | 2026-07-25