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

This isn't revolutionary, but the combination of MLX and A6000 felt surprisingly usable once I tuned around batch size.

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.

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 quant drift in mind from the start.

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

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?

Simple and boring is a valid answer.

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

This isn't revolutionary, but the combination of llama.cpp and GB200 felt surprisingly usable once I tuned around HBM constraints.

Maybe useful as a starting point for newer folks.

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

Maybe useful as a starting point: MLX on 5090, keeping long context 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.

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 speculative decoding decides everything downstream.

Maybe obvious to some of you, but it clicked for me this week.

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

Short version: TensorRT-LLM behaved well for me, but only after I stopped ignoring prefix caching.

Curious if anyone else landed in the same place.

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

Maybe I'm missing something obvious.

I'm mostly working with Mixtral in SGLang on GB200. The part I'm trying to understand is small local stacks.

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.

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

I've gone back and forth between SGLang and SGLang for Kimi. The interesting difference for me has been HBM constraints.

What would you pick if you had to live with it for a few months?

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.

Maybe useful as a starting point: Ollama on B200, keeping MoE serving in mind from the start.

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

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

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