Blackwell deployment chatter: early impressions

[kernelmike]

Did a bit of testing and thought I'd post the result here.

I tried this with Gemma using PyTorch on Threadripper box. The headline result was better than I expected, but router policy still ended up being the limiting factor.

Happy to post more details if anyone wants them.

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

[dockember]

I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model.

[audiopath66]

Did a small comparison this afternoon. My result was mixed: first run looked bad, second run looked normal. The surprise was that memory pressure showed up before throughput collapsed. The logs were somehow calmer than I was.

[edgecoast]

Did a small comparison this afternoon. My result was mixed: first run looked bad, second run looked normal. The surprise was that memory pressure show

I tested this tonight. The issue went away once I changed one setting and restarted the stack cleanly. I expected the model choice to matter more than it did.

[mintforge]

Interesting, I hadn't tried that. I ended up in about the same place using Ollama.

[orbitbench6]

My first guess would be this: If you're on MLX, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time.

[tensorriver]

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

[phaseport36]

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.

I'm happy to test one more variant if you want a second data point.

[amberpatch25]

That matches what I was seeing. That matches what I saw on GB200. The biggest change for me was simplifying the stack instead of adding one more optimization layer.