Yes, it is yet another fork. But i think it brings some interesting things to the table. It's up to you whether you want to have it built and use it or if you just want to cherrypick some of the things into your own forks. Built by GLM 5.3, GPT Sol and Q38FN. Aimed at usage with a rocm-primary system that has a unused cuda gpu sitting idle. But also provides improvements for gfx906 and adds rocm dflash2 support. The features at a glance: *~47 GiB MoE (Swift-1.5 / Qwen3.8-Flash-Next) as a daily server on 2× Radeon VII (gfx906, 2019) + one 8 GiB NVIDIA laptop card.* **What base Strata can't do — this fork does** - **Runs on gfx906 at all** — full HIP backend, built and served through every release v0.1.45 → v0.2.11 (stock: compile-check only, doesn't build as tagged) - **GEMM works** — custom single-target rocBLAS replaces the stock library that segfaults on gfx906 on first inference - **Mixed AMD + NVIDIA serving** — the NVIDIA card serves expert/prefill rows to the AMD engine over a socket; stock has no cross-vendor path - **Speculative decoding on HIP** — DFlash2 drafter runs on the AMD cards (stock: CUDA-only), with exact-match verification for sampled requests, not just greedy - **Self-healing production pair** — SIGKILL the helper: requests finish anyway, supervisor rebuilds the pair, health returns; no human touch **Speed (vs same-day stock, exclusive box, gated)** - **+49 % decode** — 34.7 vs 23.3 tok/s pooled median; 1.3× on 1024-token completions - **1.4–2.4× short-prompt reads** (200–600 token agent traffic; ≥18k prompts unchanged) - **−6.2 GiB host RAM** — helper-held experts dropped from the host arena - Honest cost on record: fresh ~16k prefill reads −12 %; long-context reads gain instead **Quality (proven, not assumed)** - Greedy 80k-token infill **byte-identical to stock** - Teacher-forced gate, 4995 positions: **top-1 100 % · KL 0.00000 · PPL equal** - Full test suite green on fresh builds (291 tests) - Every speed win promoted only through both the speed lane and the quality lane **Engineering** - Latest upstream (v0.2.11) with all fork features intact and re-verified after every merge - A/B/C-vs-stock bench harness, sha parity gates, quality yardstick, full session journal — all in-repo, anyone can re-run every claim - OpenAI + Anthropic compatible API, supervised, generation-tracked Build = pinned ROCm 6.1 + custom gfx906-xnack rocBLAS — full guide, tables and session journal in the repo: https://github.com/milpster/strata-dflash2-gfx906-mixed-rocm-cuda   submitted by   /u/milpster [link]   [comments]