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.

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.

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

Maybe obvious to some of you, but it clicked for me this week.

This is mostly for local work rather than public serving.

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

I've gone back and forth between PyTorch and LM Studio for Llama. The interesting difference for me has been speculative decoding.

Interested in tradeoffs more than fan loyalty.

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.

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

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

I'm comparing two approaches here: keep it simple on M3 Max, or squeeze more out of the stack and deal with the complexity.

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

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

I've gone back and forth between llama.cpp and MLX for Qwen. The interesting difference for me has been HBM constraints.

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

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 B200, I ended up with decent performance, but the setup only really made sense once I adjusted around workstation thermals.

Curious if anyone else landed in the same place.

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 continuous batching.

Happy to post more details if anyone wants them.

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 continuous batching decides everything downstream.

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

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.

Maybe obvious to some of you, but it clicked for me this week.