alt Hacker NewsTokens per second is almost entirely memory bandwidth at inference time, training obviously needs more compute but you can add more chips for that.
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cubefox • yesterday at 10:30 PM
According to SemiAnalysis, both inference and post-training (RLVR) is mostly memory bandwidth bound. Only pre-training is compute bound, but it now only takes a small share of overall data center capacity.
darig • yesterday at 11:28 PM
[dead]
Not quite, it's got quite a bit more complicated with agentic use cases.
Prefill (input tokens) is heavily compute bound. And the ratio of input to output continues to rise, as typically in agentic sessions you have a few tokens output for a tool call and (many) thousands of input from the tool result.
Then you have cached input tokens, which is a totally different issue, system RAM or NVMe bound.
Obviously output tokens is VRAM memory bandwidth bound, but this is less and less of the bottleneck these days for overall agentic speed.