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

I'm comparing two approaches here: keep it simple on dual 3090, or squeeze more out of the stack and deal with the complexity.

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.

Has anyone else run into this?

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

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.

Short version: TensorRT-LLM behaved well for me, but only after I stopped ignoring prefill/decode split.

Curious if anyone else landed in the same place.

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.

One thing I keep noticing is that people underestimate prefill/decode split until they actually try to run this stuff for more than one afternoon.

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

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

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

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

I'm comparing two approaches here: keep it simple on 4090, or squeeze more out of the stack and deal with the complexity.

Trying to avoid rebuilding the whole thing twice.

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.

Two paths both seem viable, but they break down differently once tail latency becomes important.

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

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

I've gone back and forth between llama.cpp and LM Studio for Llama. The interesting difference for me has been consumer GPUs.

Trying to avoid rebuilding the whole thing twice.

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

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 embeddings.

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

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 A6000, I ended up with decent performance, but the setup only really made sense once I adjusted around long context.

Curious if anyone else landed in the same place.

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.

Maybe useful as a starting point: llama.cpp on mini PC, keeping batch size in mind from the start.

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