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Thought I'd post a small recipe because a lot of threads ask similar things.
This isn't revolutionary, but the combination of vLLM and B200 felt surprisingly usable once I tuned around HBM constraints.
If someone wants the exact settings I can clean them up and post them.
I care more about stability than squeezing the last possible tokens/sec out of it.
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I hit almost the same issue last night.
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I hit almost the same issue last night.
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I tested this tonight. On 7900 XTX with llama.cpp, I saw basically the same behavior. The surprise was that memory pressure showed up before throughput collapsed.
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A couple questions before I guess too hard. What model, what runtime, and roughly what context size?
There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
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Quote:A couple questions before I guess too hard. What model, what runtime, and roughly what context size?
Thanks, that helps. I ended up in about the same place using SGLang. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
Mostly CUDA, sometimes wrong, usually measured
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Interesting, I hadn't tried that. That matches what I saw on mini PC.
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Thanks, that helps. I ended up in about the same place using TensorRT-LLM. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
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Appreciate the detail. I ended up in about the same place using MLX. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.