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?

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

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

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

Maybe useful as a starting point: llama.cpp on Threadripper box, keeping small local stacks in mind from the start.

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.

Just sharing a setup that ended up working reasonably well: Mixtral, SGLang, A6000, with a lot of attention on consumer GPUs.

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.

Just sharing a setup that ended up working reasonably well: Mistral, LM Studio, 7900 XTX, with a lot of attention on VRAM headroom.

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

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.

Short version: PyTorch behaved well for me, but only after I stopped ignoring token cost.

Happy to post more details if anyone wants them.

For context, I'm not chasing leaderboard numbers. I mostly want the system to feel consistent day to day.

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 prefill/decode split.

Would appreciate practical advice rather than theory.

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

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

Interested in tradeoffs more than fan loyalty.

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 SGLang and mini PC.

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.

Short version: Ollama behaved well for me, but only after I stopped ignoring structured output.

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

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've gone back and forth between Ollama and SGLang for Gemma. The interesting difference for me has been token cost.

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