When an author is confused about something so elementary, I can’t trust anything else they write.
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.
This is not really correct given how input token caching works and the reality of subagent workloads. You could launch many parallel subagents sharing some portion of their input tokens and use batching for that task.
1. Parallel investigation : the payoff form that is relatively small - starting K subagents assumes you have K independent avenues of investigation - and quite often that is not true. Somewhat similar to next-turn prediction using a speculative model - works well enough for 1 or 2 turns, but fails after.
2. Input caching is pretty much fixes prefill - not decode. And if you look at frontier models - for example open-weight models that can do reasoning - you are looking at longer and longer reasoning chains for heavy tool-using models. And reasoning chains will diverge very vey quickly even from the same input assuming a non-0 temp.
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.
Also there does not exist any batch size > 1 where per-request throughput is equal to bs=1. Doing any batching at all will slow all intra-batch requests down.
You don't have to work for a frontier lab to know that. You just have to be GPU poor.