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

After a few runs on 4090, I ended up with decent performance, but the setup only really made sense once I adjusted around tool calling.

Happy to post more details if anyone wants them.

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

Maybe useful as a starting point: TensorRT-LLM on 7900 XTX, keeping structured output in mind from the start.

Feel free to tear it apart if you see something dumb.

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

Short version: PyTorch behaved well for me, but only after I stopped ignoring NVFP4.

Curious if anyone else landed in the same place.

My use case is pretty ordinary: local chat, some document work, and occasional batch jobs.

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 structured output.

Curious if anyone else landed in the same place.

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 consumer GPUs.

Wondering if this is just where the whole space is heading now.

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

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 SGLang and 7900 XTX.

Would rather learn from your scars than collect my own all at once.

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

This isn't revolutionary, but the combination of Ollama and 7900 XTX felt surprisingly usable once I tuned around tool calling.

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

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

I tried this with Mistral using LM Studio on GB200. The headline result was better than I expected, but token cost still ended up being the limiting factor.

Curious if anyone else landed in the same place.

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.

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

After a few runs on mini PC, I ended up with decent performance, but the setup only really made sense once I adjusted around small local stacks.

Nothing scientific here, just practical notes from a real setup. At this point the real bottleneck is my patience.