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?

I'm especially interested in mistakes worth avoiding.

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?

I'm especially interested in mistakes worth avoiding.

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 long context becomes important.

Interested in tradeoffs more than fan loyalty.

This is mostly for local work rather than public serving. Turns out 'just one more test' was a lie.

I'm trying to sanity-check my setup.

I'm mostly working with Phi in PyTorch on 4090. The part I'm trying to understand is workstation thermals.

Not looking for the perfect answer, mostly what worked in real use.

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

I've gone back and forth between llama.cpp and vLLM for Gemma. The interesting difference for me has been speculative decoding.

Trying to avoid rebuilding the whole thing twice.

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

This keeps coming up in conversation so I figured I'd start a thread.

It feels like the conversation has shifted hard toward inference and serving details lately, and honestly that makes sense once you start dealing with INT4.

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

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

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 TensorRT-LLM and 4090.

I'm especially interested in mistakes worth avoiding.

This is mostly for local work rather than public serving.

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

I tried this with Phi using TensorRT-LLM on mini PC. 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.

Wanted to compare notes with people doing similar things.

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

Not looking for the perfect answer, mostly what worked in real use.

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.

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?

I'm especially interested in mistakes worth avoiding.

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