which local open model do you keep coming back to: what are you seeing?

[deepseeked]

What worked for me was pretty simple. If you're on SGLang, I'd confirm the version first and then reduce variables until the behavior is obvious. Once

Good point. I don't think your conclusion is far off.

[batchshed]

[quote]What worked for me was pretty simple. If you're on SGLang, I'd confirm the version first and then reduce variables until the behavior is obviou

My first guess would be this: If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious.

[cachebench]

A couple things I'd check first. If you're on TensorRT-LLM, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time.

[labnest]

Maybe, but I had the opposite result. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Could be a workload difference, but I wouldn't generalize too fast from one setup.