multimodal local stack that feels real with LM Studio

[latentpeak22]

Maybe I'm missing something obvious.

My current setup is SGLang with Threadripper box, and I keep running into KV cache pressure.

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

[phasespark]

Maybe I'm missing something obvious.

A couple questions before I guess too hard. What model, what runtime, and roughly what context size?

[thetaember38]

That matches what I was seeing. I don't think your conclusion is far off.

[byteyard]

Ran a quick check on my box. I couldn't reproduce the slowdown until I pushed context much higher.

[latentpeak22]

I'd approach it in a boring order. On 7900 XTX, the easiest win is often to back off the aggressive settings and see whether the problem survives.

[turboarch95]

I tested this tonight. On dual 3090 with PyTorch, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed.

[audiopath66]

Interesting, I hadn't tried that. I ended up in about the same place using LM Studio. The biggest change for me was simplifying the stack instead of adding one more optimization layer.

[rasterhall]

Interesting, I hadn't tried that. I ended up in about the same place using LM Studio. The biggest change for me was simplifying the stack instead of a

My first guess would be this: On B200, the easiest win is often to back off the aggressive settings and see whether the problem survives. Not glamorous advice, but repeatable baselines save a lot of time.

[cudapoint]

A couple things I'd check first. If you're on Ollama, 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.

[vectorshed43]

I don't think that's the whole story. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Worth testing the simpler explanation before adopting the more interesting one.