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 tool calling.

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.

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

This isn't revolutionary, but the combination of Ollama and mini PC felt surprisingly usable once I tuned around prefill/decode split.

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.

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

After a few runs on 5090, I ended up with decent performance, but the setup only really made sense once I adjusted around structured output.

Happy to post more details if anyone wants them.

I expected the biggest difference to come from the model, but the stack around it mattered at least as much.

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 Ollama and 7900 XTX.

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

I figured I'd ask here before changing everything.

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

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

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

Wanted to compare notes with people doing similar things.

I'm mostly working with Mixtral in MLX on 5090. The part I'm trying to understand is tokens/sec.

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

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 PyTorch and vLLM for FLUX. The interesting difference for me has been consumer GPUs.

Interested in tradeoffs more than fan loyalty.

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.

I'm comparing two approaches here: keep it simple on 5090, 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?

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

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

I tried this with Mixtral using Ollama on M3 Max. The headline result was better than I expected, but MoE serving still ended up being the limiting factor.

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

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 tool calling.

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

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

I expected the biggest difference to come from the model, but the stack around it mattered at least as much.