Posting this in case it saves someone else a little time.

I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about speculative decoding.

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.

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

After a few runs on M3 Max, I ended up with decent performance, but the setup only really made sense once I adjusted around tool calling.

Curious if anyone else landed in the same place.

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 embeddings.

Happy to post more details if anyone wants them.

Posting this in case it saves someone else a little time.

I'm helping a small team set up a local workflow. We don't need a giant stack, but we do care about NVFP4.

Simple and boring is a valid answer.

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 SGLang and A6000.

Simple and boring is a valid answer.

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 quant drift becomes important.

Trying to avoid rebuilding the whole thing twice.

I expected the biggest difference to come from the model, but the stack around it mattered at least as much.

Curious what people here are seeing.

My current setup is LM Studio with 7900 XTX, and I keep running into NVFP4.

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

I expected the biggest difference to come from the model, but the stack around it mattered at least as much.

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

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

Interested in tradeoffs more than fan loyalty.

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: Kimi, LM Studio, 7900 XTX, with a lot of attention on continuous batching.

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.

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

Maybe useful as a starting point: TensorRT-LLM on 7900 XTX, keeping consumer GPUs in mind from the start.

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