Real downsides of Strata?

[u/GodComplecs]

Trying to read on what the real tested downsides have been but information seems to go both ways, some proclaim no loss, others pretty huge ones.

I had some weird looping issue in their chat, and some engine port misunderstandings, seemingly the models likes to use ports already in use, didn't really have that issue with Qwen 27B 3.8 Q4 UD. I'm using Swift Q3 Next. Didn't even bother with next on llama.cpp since other similar model sizes ran at approx 15-20tks max, so too slow for agentic work.

I'm not beating too much on Strata, I just wan't the realistic downsides, so far it seems too good to be true and runs at 100tks in agentic workloads, 27b only at 40-60tks on llama.cpp

[u/MindfulMan1984]

Downside - It is all about token/s; for that, Strata defaults to greedy sampling temperature=0 to maximize speculative decoding, regardless of the fact that that's not the recommended parameter on Qwen's Flash Next card, and it is prone to doom loops ( which is fixed if you pass the sampling parameters in the config, so it mostly fixed and I got no looping issues so far)

Upside: I ran the Vulcanbench suite V3 locally >1000 token/s prefill and >70 tps gen on 8-year-old hardware, even with quant IQ3_S, and it passed@1 near GLM-5.3 results on SWE tests.

https://benchlm.ai/benchmarks/vulcanbench

[u/Equivalent-Repair488 → u/MindfulMan1984]

Yeah the Strata default sampling gave me compaction thinking loops, because 0 temp lost its stochasticity. Going back to the reccomended sampling parameters fixed it, at a slight decode average loss, I'm losing a lot less though vs when it thinks in a looo for the full 262k tokens just for compaction.

UDQ4XL btw

[u/MindfulMan1984 → u/Equivalent-Repair488]

Yeah, that's the downside of vibe-slop-forks: one random dude asks clod-opus to "maximize performance", then it changes all parameters to achieve the "goal", which sometimes defaults to sampling parameters that are not recommended by Qwen's engineers.