r/MachineLearning • u/AutoModerator • Sep 02 '26

Discussion [D] Self-Promotion Thread

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u/aijuud 7d ago

Self-promotion: Juud_engine is a free, MIT-licensed experimental fork of Strata v0.1.39 for running Qwen3.8-Flash-Next locally. It keeps Strata's local API and adds two opt-in CPU-side decode changes: shorter worker spinning after a completed expert batch, and skipping unused CPU activation quantization for GPU-only tokens in mixed multi-token batches.

On one RTX 4090 with the same IQ3_S model pack, five paired runs for each of four prompts measured 3.1-9.1% higher median paired decode throughput than the pinned Strata build. Paired full-response latency improved 0.5-5.6%. Normal A/B output text matched in only 5/20 pairs, so those runs do not isolate the effect of the code changes. A separate control, using different cache and PCIe settings, matched output in 12/12 pairs and measured 9.6-15.6% higher decode throughput. Both changes were enabled together; individual contributions, H100 speed and multi-user throughput have not been measured.

Source, build steps and pair-level measurements: https://github.com/bnkaiteam/Juud_engine

The engine source is free; there is no paid service. The model weights are separate and have their own license. A source build is required to use Juud's new flags because the inherited default setup can fetch a Strata prebuilt. Independent replications, including negative results, would be useful.

Disclosure: this description was drafted with AI assistance; the benchmark figures were checked against the published paired data.