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

Short version: LM Studio behaved well for me, but only after I stopped ignoring structured output.

Nothing scientific here, just practical notes from a real setup.

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

Two paths both seem viable, but they break down differently once tool calling becomes important.

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

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 tail latency decides everything downstream.

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'm trying to sanity-check my setup.

I'm mostly working with Mistral in MLX on 5090. The part I'm trying to understand is continuous batching.

Not looking for the perfect answer, mostly what worked in real use.

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 PyTorch and dual 3090 felt surprisingly usable once I tuned around tail latency.

Maybe useful as a starting point for newer folks.

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 PyTorch and 7900 XTX.

I'm especially interested in mistakes worth avoiding.

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 router policy.

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

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

I've gone back and forth between TensorRT-LLM and llama.cpp for Mistral. The interesting difference for me has been batch size.

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

The part that surprised me was how much the boring details mattered more than the model choice.

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?

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

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

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

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

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