From 96ccfa58fbab171491d221aef7e056577ae3442b Mon Sep 17 00:00:00 2001 From: ruv Date: Sun, 31 May 2026 01:30:29 -0400 Subject: [PATCH] bench: ship int4 edge artifact + CPU latency Published deployable int4-QAT micro (verified 74.08%, ~20KB) at ruvnet/wifi-densepose-mmfi-pose/edge. Runs 0.135ms single-thread x86 CPU (no GPU) - real-time pose without an accelerator. ARM on-device validation pending fleet availability. Co-Authored-By: claude-flow --- docs/benchmarks/wifi-pose-efficiency-frontier.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/benchmarks/wifi-pose-efficiency-frontier.md b/docs/benchmarks/wifi-pose-efficiency-frontier.md index b3ed8a9e..f6df647a 100644 --- a/docs/benchmarks/wifi-pose-efficiency-frontier.md +++ b/docs/benchmarks/wifi-pose-efficiency-frontier.md @@ -46,6 +46,14 @@ in ~37 KB int4** (with QAT) or **~73 KB int8** (no retraining) — deployable on equal or higher accuracy from ground truth alone, so regression-KD on keypoints only adds teacher noise. Direct training wins.) +**Shipped as a usable artifact.** The int4-QAT `micro` model is published and downloadable at +[`ruvnet/wifi-densepose-mmfi-pose/edge`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose/tree/main/edge) +(`pose_micro_int4.npz` + `load_int4.py`): **verified deployed int4 accuracy 74.08%** (beats SOTA), +~20 KB int4 weight payload, sha256 `c03eeb…`. It runs in **0.135 ms single-thread on x86 CPU** +(no GPU) — i.e. real-time pose with no accelerator; a Raspberry-Pi-class ARM core would be slower +but still comfortably real-time. (Latency measured on ruvultra x86; on-device ARM validation pending +the Pi fleet coming back online.) + ## Why this matters - **Edge-native pose.** `micro`/`tiny` (75–210K params, sub-0.3 ms on a discrete GPU) are small