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: Llama, llama.cpp, B200, with a lot of attention on continuous batching.

Maybe useful as a starting point for newer folks.

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: Llama, TensorRT-LLM, M3 Max, with a lot of attention on long context.

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

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

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 reranking decides everything downstream.

Curious whether that lines up with what other people are seeing.

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.

My current setup is MLX with A6000, and I keep running into consumer GPUs.

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

Wanted to compare notes with people doing similar things.

I'm trying to decide whether to keep the current stack or change course. Main question is how people here are handling MoE serving.

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

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

I've gone back and forth between LM Studio and TensorRT-LLM for Mistral. The interesting difference for me has been tool calling.

Interested in tradeoffs more than fan loyalty.

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 router policy.

Curious whether that lines up with what other people are seeing.

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 TensorRT-LLM and Threadripper box felt surprisingly usable once I tuned around consumer GPUs.

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

I figured I'd ask here before changing everything.

My current setup is MLX with GB200, and I keep running into embeddings.

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

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

I've gone back and forth between PyTorch and Ollama for Qwen. The interesting difference for me has been batch size.

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