I'm trying to sanity-check my setup.

My current setup is TensorRT-LLM with 4090, and I keep running into continuous batching.

If you've tested something similar, what did you keep and what did you stop bothering with?

This is mostly for local work rather than public serving.

Posting this in case it saves someone else a little time.

If you were starting from scratch today for this use case, what would you choose first and what would you ignore?

Simple and boring is a valid answer.

Did a bit of testing and thought I'd post the result here.

Short version: TensorRT-LLM behaved well for me, but only after I stopped ignoring router policy.

Happy to post more details if anyone wants them.

Spent some time on this today and wanted to compare notes.

I've gone back and forth between PyTorch and TensorRT-LLM for Kimi. The interesting difference for me has been workstation thermals.

What would you pick if you had to live with it for a few months?

Did a bit of testing and thought I'd post the result here.

I tried this with FLUX using Ollama on 5090. The headline result was better than I expected, but prefix caching still ended up being the limiting factor.

Happy to post more details if anyone wants them.

This keeps coming up in conversation so I figured I'd start a thread.

It feels like the conversation has shifted hard toward inference and serving details lately, and honestly that makes sense once you start dealing with speculative decoding.

Curious whether that lines up with what other people are seeing.

This is mostly for local work rather than public serving.

Thought I'd post a small recipe because a lot of threads ask similar things.

Just sharing a setup that ended up working reasonably well: Kimi, TensorRT-LLM, GB200, with a lot of attention on tokens/sec.

If someone wants the exact settings I can clean them up and post them.

Spent some time on this today and wanted to compare notes.

I've gone back and forth between LM Studio and SGLang for Kimi. The interesting difference for me has been speculative decoding.

Interested in tradeoffs more than fan loyalty.

This is mostly for local work rather than public serving. Turns out 'just one more test' was a lie.

Posting this in case it saves someone else a little time.

I'm not trying to build a perfect architecture diagram. I just want a sane starting point around vLLM and B200.

Simple and boring is a valid answer.

I care more about stability than squeezing the last possible tokens/sec out of it.

Thought I'd post a small recipe because a lot of threads ask similar things.

This isn't revolutionary, but the combination of TensorRT-LLM and A6000 felt surprisingly usable once I tuned around MoE serving.

Maybe useful as a starting point for newer folks.