> If copying user tokens was the bottle neck, batching would not achieve any speed up.

Reality is more complex. As context length grows your KV cache becomes large and will begin to dominate your total FLOPs (and hence bytes loaded). The issue with KV cache is you cannot batch it because only one user can use it, unlike static layer weights where you can reuse them across multiple users.

Emerging sparse attention techniques can greatly relieve this issue though the extent to which frontier labs deploy them is uncertain. Deepseek v3.2 uses sparse attention though I don't know off hand how much this reduces KV cache FLOPs and associated memory bandwidth.

They failed to grasp the very fundamental point of batching, which is sharing model weights between requests. For more context, this wasn't just one person's mistake, several AI twitter personalities proposed this 'Claude Opus fast = small batching' hypothesis. What I find funny is how confident these AI influencers were, while the people who actually work on LLM serving at frontier labs said nothing. The people who genuinely understand this and work at frontier labs stay quiet. The rest is simply noise.

> The main explanation of bottleneck is wrong: it’s the model weights which dominate memory bandwidth (and hence why batching multiple requests in a single pass increases total throughput). If copy user tokens was the bottle neck, batching would not achieve any speed up.

Inference is memory-bound only at low batch sizes. At high batch sizes it becomes compute-bound. There's a certain threshold where stuffing more requests in a batch will slow down every request in isolation even though it may still increase the number of tokens/second across the whole batch for all request in aggregate.