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 llama.cpp and B200.

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

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

The more I work with local setups, the more it seems like batch size decides everything downstream.

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

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: Mistral, TensorRT-LLM, GB200, with a lot of attention on INT4.

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

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.

This isn't revolutionary, but the combination of vLLM and B200 felt surprisingly usable once I tuned around HBM constraints.

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

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

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 quant drift.

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

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

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

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

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.

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

Two paths both seem viable, but they break down differently once tail latency becomes important.

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

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

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

I tried this with Llama using SGLang on Threadripper box. The headline result was better than I expected, but tokens/sec still ended up being the limiting factor.

Happy to post more details if anyone wants them.

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

Two paths both seem viable, but they break down differently once KV cache pressure becomes important.

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

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

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

The more I work with local setups, the more it seems like continuous batching decides everything downstream.

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

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