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

I tried this with Phi using llama.cpp on mini PC. The headline result was better than I expected, but INT4 still ended up being the limiting factor.

Curious if anyone else landed in the same place.

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

After a few runs on Threadripper box, 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.

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

Two paths both seem viable, but they break down differently once MoE serving becomes important.

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

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 M3 Max felt surprisingly usable once I tuned around KV cache pressure.

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

This is mostly for local work rather than public serving.

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.

Maybe useful as a starting point: PyTorch on GB200, keeping HBM constraints in mind from the start.

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

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day. At this point the real bottleneck is my patience.

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 router policy.

Curious if anyone else landed in the same place.

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: Kimi, TensorRT-LLM, A6000, with a lot of attention on consumer GPUs.

Maybe useful as a starting point for newer folks.

Curious what people here are seeing.

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

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

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

I tried this with Qwen using MLX on B200. The headline result was better than I expected, but tokens/sec still ended up being the limiting factor.

Happy to post more details if anyone wants them.