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 consumer GPUs.

I'm especially interested in mistakes worth avoiding.

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

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 token cost.

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

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

This isn't revolutionary, but the combination of LM Studio and B200 felt surprisingly usable once I tuned around long context.

Maybe useful as a starting point for newer folks.

This is mostly for local work rather than public serving.

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

This isn't revolutionary, but the combination of vLLM and B200 felt surprisingly usable once I tuned around long context.

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.

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, PyTorch, GB200, with a lot of attention on KV cache pressure.

Maybe useful as a starting point for newer folks.

This is mostly for local work rather than public serving.

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

One thing I keep noticing is that people underestimate small local stacks until they actually try to run this stuff for more than one afternoon.

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

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 VRAM headroom.

I'm especially interested in mistakes worth avoiding.

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 KV cache pressure.

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.

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

This isn't revolutionary, but the combination of SGLang and A6000 felt surprisingly usable once I tuned around MoE serving.

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.

This isn't revolutionary, but the combination of SGLang and Threadripper box felt surprisingly usable once I tuned around HBM constraints.

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