> The model is running so hot, that it shoots past the goal and starts looping

later:

> My latest experiment was setting up vLLM (the gold standard for production and concurrent serving) and even with an NVLink (175GBP) and tensor parallelism turned on, it was 3 tokens/second slower than llama.cpp during generation for an equivalent setup.

In all my tests, getting vllm to run is worth it. It was the single biggest thing, that helped for looping issues, agents going whack and losing focus on the task, long context being essentially useless.

FP8 model, unquantized cache in vllm an you have a league better overall experience, with any other stack I tested. Then, you can actually focus on using the model for other things and stop tinkering with settings.