From 4ea9ddc0f0af0f4fd08873057b4c5e9435608f3b Mon Sep 17 00:00:00 2001
From: adapt-L <110795948+adapt-L@users.noreply.github.com>
Date: Fri, 10 Jul 2026 01:41:06 -0400
Subject: [PATCH] =?UTF-8?q?README:=20community=20benchmark=20=E2=80=94=20E?=
 =?UTF-8?q?pyc=209654=20ES,=20PCIe3=20NVMe=20(0.31=20tok/s,=20hit=2035%)?=
 =?UTF-8?q?=20(#17)?=
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I think I am just disk bandwidth limited, CPU's are at like 65% average. Might consider upgrading to 4x PCIe5 x4 ssds...
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 README.md | 1 +
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diff --git a/README.md b/README.md
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@@ -139,6 +139,7 @@ Real numbers from real machines, stock build (`setup.sh`, gcc 13), greedy decodi
 | Intel Core Ultra 7 270K Plus (24 threads) · WSL2 · 24 GB RAM · NVMe VHDX ([#2](https://github.com/JustVugg/colibri/issues/2)) | 1.96 GB/s buffered · 2.74 GB/s O_DIRECT | default | 0.07 tok/s · expert hit 3–4% · RSS 14.1 GB |
 | 〃 | 〃 | `--topp 0.7` | **0.11 tok/s** · expert hit 11% · RSS 14.7 GB |
 | Apple M5 Max (18 cores) · macOS · 128 GB unified · internal SSD ([#4](https://github.com/JustVugg/colibri/issues/4), [#5](https://github.com/JustVugg/colibri/issues/5)) | 14.2 GB/s O_DIRECT | default, MTP off | **1.06 tok/s** · expert hit 23% · RSS 21.8 GB |
+| Epyc 9654 ES · Linux · 4x16GB DDR5-4800-rdimm · Samsung PCIe Gen3 x4 NVME SSD | MTP=1 DIRECT=1 | default | 0.31 tok/s · expert hit 35% · RSS 21.52 GB |
 
 Takeaways: with 24 GB of RAM the engine auto-caps the expert cache to 2 slots/layer, so decode stays cold even on a disk 2–2.7× faster than the dev box — **on small-RAM machines the RAM cap, not the disk, is the binding constraint**, exactly as the table above predicts; `--topp 0.7` alone bought a clean 1.6× end-to-end speedup. The M5 Max datapoint lands right on the table's second row: **~1 tok/s of a 744B model on a laptop SSD** — and its 14 GB/s disk shifts the bottleneck back to RAM budget and kernels.