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 prefill/decode split decides everything downstream.

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

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 M3 Max, or squeeze more out of the stack and deal with the complexity.

Interested in tradeoffs more than fan loyalty.

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

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

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 reranking decides everything downstream.

Curious whether that lines up with what other people are seeing.

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 SGLang and 5090.

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

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

I tried this with Phi using PyTorch on 5090. The headline result was better than I expected, but batch size still ended up being the limiting factor.

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 TensorRT-LLM and vLLM for FLUX. The interesting difference for me has been INT4.

Interested in tradeoffs more than fan loyalty.

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.

Two paths both seem viable, but they break down differently once VRAM headroom becomes important.

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

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.

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 structured output decides everything downstream.

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

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.

I'm especially interested in mistakes worth avoiding.