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

After a few runs on A6000, I ended up with decent performance, but the setup only really made sense once I adjusted around structured output.

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

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: DeepSeek, vLLM, A6000, with a lot of attention on reranking.

Maybe useful as a starting point for newer folks.

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

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

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

Maybe useful as a starting point: TensorRT-LLM on mini PC, keeping workstation thermals in mind from the start.

Maybe useful as a starting point for newer folks.

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

Wanted to compare notes with people doing similar things.

My current setup is PyTorch with B200, and I keep running into workstation thermals.

Would appreciate practical advice rather than theory.

The part that surprised me was how much the boring details mattered more than the model choice. At this point the real bottleneck is my patience.

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

After a few runs on dual 3090, I ended up with decent performance, but the setup only really made sense once I adjusted around workstation thermals.

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.

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.

Curious what people here are seeing.

My current setup is Ollama with 4090, and I keep running into tool calling.

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

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 Threadripper box.

I'm especially interested in mistakes worth avoiding.

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

I'm trying to sanity-check my setup.

My current setup is LM Studio with dual 3090, and I keep running into prefill/decode split.

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

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

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 long context.

Curious if anyone else landed in the same place.