alt Hacker NewsIt comes from batching and multiple streams on a GPU. More people sharing 1 GPU makes everyone run slower but increases overall token throughput.
Mathematically it comes from the fact that this transformer block is this parallel algorithm. If you batch harder, increase parallelism, you can get higher tokens/s. But you get less throughput. Simultaneously there is also this dial that you can speculatively decode harder with fewer users.
Its true for basically all hardware and most models. You can draw this Pareto curve of how much throughput per GPU vs how many tokens per second per stream. More tokens/s less total throughput.
See this graph for actual numbers:
Token Throughput per GPU vs. Interactivity gpt-oss 120B • FP4 • 1K / 8K • Source: SemiAnalysis InferenceMAX™
> If you batch harder, increase parallelism, you can get higher tokens/s. But you get less throughput. Simultaneously there is also this dial that you can speculatively decode harder with fewer users.
I think you skipped the word “total throughout” there right? Cause tok/s is a measure of throughput, so it’s clearer to say you increase throughput/user at the expense of throughput/gpu.
I’m not sure about the comment about speculative decode though. I haven’t served a frontier model but generally speculative decode I believe doesn’t help beyond a few tokens, so I’m not sure you can “speculatively decode harder” with fewer users.