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

After a few runs on GB200, I ended up with decent performance, but the setup only really made sense once I adjusted around MoE serving.

Curious if anyone else landed in the same place.

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

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

I'm comparing two approaches here: keep it simple on GB200, or squeeze more out of the stack and deal with the complexity.

Interested in tradeoffs more than fan loyalty.

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

One thing I keep noticing is that people underestimate batch size until they actually try to run this stuff for more than one afternoon.

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

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

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

I'm comparing two approaches here: keep it simple on 7900 XTX, or squeeze more out of the stack and deal with the complexity.

What would you pick if you had to live with it for a few months? I keep promising myself this is the last rebuild.

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 7900 XTX felt surprisingly usable once I tuned around embeddings.

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

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

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

Maybe useful as a starting point: LM Studio on 7900 XTX, keeping consumer GPUs in mind from the start.

Maybe useful as a starting point for newer folks.

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 tokens/sec.

Maybe obvious to some of you, but it clicked for me this week.

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

I figured I'd ask here before changing everything.

I'm mostly working with Llama in Ollama on 4090. The part I'm trying to understand is workstation thermals.

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

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?

I'm especially interested in mistakes worth avoiding.

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

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

I've gone back and forth between TensorRT-LLM and TensorRT-LLM for Llama. The interesting difference for me has been reranking.

Interested in tradeoffs more than fan loyalty.

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