Spot the errors in this 'API is slow because of network and tokenizer' explanation

**LLM inference latency** has two phases. Prefill reads your whole prompt in one parallel pass and is fast. Decode generates the answer one token at a time, and each step waits for the previous one. Decode is where the time goes. The explanation under review blames three things: network round trip, tokenizer overhead, and a fast server. Two of those numbers are wildly off, and the third is backwards. Network round trip to a major provider is tens of milliseconds total, not seconds. Tokenization is microseconds per token, far below anything you would notice. The real bottleneck is decode itself. A single chat request gets roughly 50 to 150 tokens per second, because the model must read its large key-value cache from GPU memory before producing each new token. That memory traffic, not arithmetic, sets the pace. Do the math: 500 tokens at 50 tokens per second is 10 seconds. That matches the observed 8 to 12 seconds almost exactly. So decode accounts for essentially all of the wall clock, and the network plus tokenizer story is the classic wrong diagnosis.

**Decode dominates.** The explanation pins the latency on network and tokenizer, but both are off by orders of magnitude, and the claim that decode is subsecond is simply false. Network round trip to a major provider is 50 to 100 milliseconds total. Tokenization runs in microseconds per token. Neither registers against an 8 to 12 second wall clock. They are rounding error, not the root cause. The true bottleneck is autoregressive decode. Generation happens one token at a time, sequentially. Each step must stream the model weights and the growing key-value cache from HBM. So per-request throughput is bounded by memory bandwidth, not compute. A single chat request realistically sustains 50 to 150 tokens per second. Run the arithmetic. Five hundred tokens at 50 tokens per second is 10 seconds. That lands inside the observed range. The thousands-per-second figure describes aggregate server throughput across a large batch, not the latency one user sees. The diagnosis to give: decode is memory-bandwidth-bound and inherently sequential. Fix it with faster generation, not faster networking. Speculative decoding, larger batches, and shorter outputs move the needle. Shaving 50 milliseconds off the network does nothing.