LocalLLaMA treated this less as a speed chart and more as a question about completion quality under a messy real prompt. On the same MacBook Pro M5 Max, Qwen 3.6 27B wrote more and faster, but Gemma 4 31B finished the game logic with far fewer tokens.
LocalLLaMA cared less about headline speed than a Qwen3.6 setup on one RTX 3090 that reached 218K context and stopped crashing on long tool outputs.
Why it matters: kernel work is what decides whether long-context and edge-side agent systems stay theoretical or become cheap enough to run. Qwen says FlashQLA delivers 2-3x forward speedup and 2x backward speedup over the FLA Triton kernel on NVIDIA Hopper.
LocalLLaMA reacted to this post because it brought hard numbers, not vendor marketing: a dual RTX 5060 Ti 16GB setup pushing Qwen3.6 27B to roughly 60 tok/s with a 204k context window.
The top comment went straight to the CP joke, but the post held because the technical claim was concrete: 2-3x forward speedups and 2x backward speedups for GDN chunked prefill, aimed at long-context and edge-side agentic inference.
LocalLLaMA got animated because the post promised something people can feel immediately: less reasoning drag. A user claims a small GBNF constraint cut Qwen3.6 token burn hard enough to speed up long tasks without wrecking benchmark scores.
The community liked this post for the same reason it immediately started arguing with it: it had real numbers. Q4_K_M came out looking like the practical sweet spot, but commenters quickly pushed on error bars, KV-cache settings, and whether the reported scores made sense at all.
This was not just another “local models are bad” rant. The thread blew up because it mixed a blunt reality check with a serious counterargument: some of the pain comes from small models, but a lot of it may come from the harness wrapped around them.
LocalLLaMA latched onto a very concrete claim: if a 27B model fits entirely in VRAM across two mismatched cards, even a weak second GPU can be better than spilling into system RAM for long-context decoding.
The spark in LocalLLaMA was not the raw score alone. The post landed because a 38.2% Terminal-Bench 2.0 result for Qwen 3.6-27B was framed as roughly late-2025 frontier quality, putting air-gapped and privacy-heavy coding teams into a new decision zone.
LocalLLaMA did not treat Luce DFlash as another benchmark screenshot. The post took off because it promised almost 2x mean throughput for Qwen3.6-27B on a single RTX 3090, with no retraining and enough memory engineering to keep long-context local inference practical.
LocalLLaMA was interested for a reason beyond a flashy speed number. A post claiming 105-108 tps and a full 256k native context window for Qwen3.6-27B-INT4 on a single RTX 5090 turned the thread into a practical discussion about how much quality survives once local inference gets this fast.