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

Maybe useful as a starting point: SGLang on dual 3090, keeping token cost in mind from the start.

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

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

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 M3 Max.

I'm especially interested in mistakes worth avoiding.

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

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

I'm trying to sanity-check my setup.

I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling KV cache pressure.

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

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day. I keep promising myself this is the last rebuild.

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

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

Trying to avoid rebuilding the whole thing twice.

I figured I'd ask here before changing everything.

I'm mostly working with FLUX in LM Studio on A6000. 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?

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

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

Maybe useful as a starting point: LM Studio on 4090, keeping consumer GPUs 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.

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

I'm comparing two approaches here: keep it simple on B200, 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?

This is mostly for local work rather than public serving.

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 KV cache pressure.

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.

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

Trying to avoid rebuilding the whole thing twice.

I'm trying to sanity-check my setup.

My current setup is PyTorch with 5090, and I keep running into prefix caching.

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