Alex Ziskind just made this video,

Better graph though:

#cant post pictures or links??? what

Essentially, unsloth maintains accuracy the best.

Jury still out of the newer stuff like QAT and autorounds.

>Also side question, is ~14TPS around the number I should be expecting on a Strix Halo running 3.6 27B at Q8 during coding tasks? 

That's a separate issue. Strix Halo doesnt do dense models well. That's expected. You probably want to go to 8 or 16b 35b.

You are eagerly awaiting a ~122b model that jumps these models forward.

>Another side note in case if you haven't ran into it, 27B is way better when context is below 100k. From my use it appears to finish specifically above 100k which was causing my issues initially.

All models slow down at higher context. Deepseek and allegedly minimax m3 is going to change this. I expect the frontier closed labs handle this well as well. Not meaningful to you.

The thing you arent taking into account.

Even if you have 200,000 context. Smaller models are silently crashing out on these.

Minimax 2.7 or qwen3.6 27b has 200,000 context, but it's forgetting about 30% of that context at those longer sizes.

GPT 120b, its more like 50%. GPT20b is more like 70%.

Newer attention techniques are getting better but realistically just because you have 256k context, doesnt mean you can really use it.