Thought I'd post a small recipe because a lot of threads ask similar things.

This isn't revolutionary, but the combination of llama.cpp and 7900 XTX felt surprisingly usable once I tuned around prefill/decode split.

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

Thought I'd post a small recipe because a lot of threads ask similar things.

This isn't revolutionary, but the combination of Ollama and GB200 felt surprisingly usable once I tuned around tool calling.

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

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.

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 is mostly for local work rather than public serving.

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

Short version: Ollama behaved well for me, but only after I stopped ignoring small local stacks.

Happy to post more details if anyone wants them.

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

I tried this with DeepSeek using llama.cpp on 5090. The headline result was better than I expected, but MoE serving still ended up being the limiting factor.

Nothing scientific here, just practical notes from a real setup.

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

One thing I keep noticing is that people underestimate token cost 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.

This is mostly for local work rather than public serving.

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

I'm comparing two approaches here: keep it simple on A6000, 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.

I figured I'd ask here before changing everything.

My current setup is PyTorch with GB200, and I keep running into VRAM headroom.

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

This is mostly for local work rather than public serving.

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 MoE serving.

Happy to post more details if anyone wants them. The logs were somehow calmer than I was.

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 token cost decides everything downstream.

Wondering if this is just where the whole space is heading now.

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.