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 speculative decoding.

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

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: TensorRT-LLM on 5090, keeping INT4 in mind from the start.

Maybe useful as a starting point for newer folks.

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 llama.cpp and llama.cpp for Gemma. The interesting difference for me has been structured output.

Interested in tradeoffs more than fan loyalty.

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 LM Studio and llama.cpp for Llama. The interesting difference for me has been prefill/decode split.

What would you pick if you had to live with it for a few months? I keep promising myself this is the last rebuild.

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 prefix caching.

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

This is mostly for local work rather than public serving.

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 VRAM headroom.

Would appreciate practical advice rather than theory.

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

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 vLLM and GB200.

I'm especially interested in mistakes worth avoiding.

This is mostly for local work rather than public serving.

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 small local stacks.

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

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

I've gone back and forth between vLLM and PyTorch for Mixtral. The interesting difference for me has been speculative decoding.

Interested in tradeoffs more than fan loyalty.

Maybe I'm missing something obvious.

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

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