Comparison
Time per Output Token vs vLLM
Time per Output Token and vLLM are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Time per Output Token
Time per Output Token comes up when the question is fundamentally about inference.
A 70B model on H100: TPOT ~25ms (~40 tokens/sec).
When you would reach for vLLM
vLLM comes up when the question is fundamentally about infrastructure.
Serving Llama 3 70B at high QPS on 4 H100s with vLLM.
Frequently asked
What is the difference between Time per Output Token and vLLM?
Time per Output Token: Time per output token (TPOT) is the average wall-clock delay between consecutive generated tokens during streaming. Determines how fast text appears once generation starts. vLLM: vLLM is an open-source high-throughput LLM serving engine. Its PagedAttention KV cache manager is the reason it dramatically outperforms naive serving setups.
When should I use Time per Output Token vs vLLM?
Time per Output Token is the right concept when you are focused on inference. vLLM applies when you are focused on infrastructure.
Are Time per Output Token and vLLM the same thing?
No. Time per Output Token is inference; vLLM is infrastructure. They are related but address different parts of the AI stack.