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 small local stacks decides everything downstream.

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

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

I tried this with Mixtral using SGLang on 7900 XTX. The headline result was better than I expected, but structured output still ended up being the limiting factor.

Happy to post more details if anyone wants them.

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.

Two paths both seem viable, but they break down differently once small local stacks becomes important.

Trying to avoid rebuilding the whole thing twice.

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.

Maybe useful as a starting point: MLX on B200, keeping token cost in mind from the start.

Maybe useful as a starting point for newer folks.

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

Has anyone else run into this?

I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling prefill/decode split.

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

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

Two paths both seem viable, but they break down differently once INT4 becomes important.

Interested in tradeoffs more than fan loyalty.

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.

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

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

Curious what people here are seeing.

I'm mostly working with Phi in PyTorch on A6000. The part I'm trying to understand is VRAM headroom.

Would appreciate practical advice rather than theory.

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.

If I had to summarize the lesson so far, it would be that clean setup and observability beat clever guesses.

Maybe I'm missing something obvious.

I'm mostly working with Llama in MLX on B200. The part I'm trying to understand is tool calling.

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