framework overhead in real deployments

[batchsize]

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 VRAM headroom.

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

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

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

[labnest]

I'd approach it in a boring order. If you're on llama.cpp, I'd confirm the version first and then reduce variables until the behavior is obvious. Once you know the plain path works, then add the faster path back in piece by piece.

[vectorcrate98]

I'd approach it in a boring order. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at embeddings.

[audiopoint18]

worth checking the driver first.

[northquill]

A couple things I'd check first. Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at workstation thermals. Not glamorous advice, but repeatable baselines save a lot of time.

[batchwire]

I don't think that's the whole story. I get why that seems right, but on 5090 the bottleneck felt elsewhere.

[syshouse]

Appreciate the detail. I ended up in about the same place using MLX.

[visionforge]

Did a small comparison this afternoon. I couldn't reproduce the slowdown until I pushed context much higher. The main thing that moved was latency, not quality.

[sanevault]

My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at small local stacks. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it.

[mlxgarden]

worth checking the driver first.