Posting this in case it saves someone else a little time.

I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about long context.

Simple and boring is a valid answer.

This is mostly for local work rather than public serving.

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

I'm comparing two approaches here: keep it simple on 4090, or squeeze more out of the stack and deal with the complexity.

Interested in tradeoffs more than fan loyalty.

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

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, LM Studio, dual 3090, with a lot of attention on tool calling.

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 structured output.

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

This is mostly for local work rather than public serving.

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

I've gone back and forth between MLX and MLX for Gemma. The interesting difference for me has been prefill/decode split.

Trying to avoid rebuilding the whole thing twice.

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

This isn't revolutionary, but the combination of SGLang and A6000 felt surprisingly usable once I tuned around tool calling.

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

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.

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

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.

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

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

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: PyTorch on Threadripper box, keeping small local stacks in mind from the start.

Maybe useful as a starting point for newer folks.

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

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

Short version: Ollama behaved well for me, but only after I stopped ignoring batch size.

Happy to post more details if anyone wants them.