Wanted to compare notes with people doing similar things.

I'm mostly working with Mistral in MLX on mini PC. The part I'm trying to understand is tokens/sec.

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.

I'm trying to sanity-check my setup.

My current setup is Ollama with B200, and I keep running into batch size.

Would appreciate practical advice rather than theory.

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

This isn't revolutionary, but the combination of TensorRT-LLM and mini PC felt surprisingly usable once I tuned around VRAM headroom.

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

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 tokens/sec.

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.

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.

I'm not trying to build a perfect architecture diagram. I just want a sane starting point around MLX and 4090.

Simple and boring is a valid answer.

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.

After a few runs on GB200, I ended up with decent performance, but the setup only really made sense once I adjusted around reranking.

Curious if anyone else landed in the same place.

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.

I've gone back and forth between PyTorch and SGLang for Phi. The interesting difference for me has been INT4.

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.

Wanted to compare notes with people doing similar things.

I'm mostly working with Phi in llama.cpp 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?

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

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 workstation thermals.

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

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 helping a small team set up a local workflow. We don't need a giant stack, but we do care about tail latency.

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

This is mostly for local work rather than public serving.