Training gets the headlines, but inference is where most AI budgets are ultimately spent — every user query runs through it, every hour of every day. The NVIDIA H200 is built with that economic reality in mind, and its design choices target the things that make inference expensive.
Key Takeaways
- Inference cost, not training, dominates most production AI budgets.
- Inference is usually memory-bound — capacity and bandwidth rule.
- The H200's large HBM3e serves bigger models on fewer GPUs.
- Measure success in cost per token at target latency.
01Memory is the inference bottleneck
Modern inference is frequently memory-bound, not compute-bound. The model weights and the growing key-value cache must stay in fast memory, and when they spill, latency and cost climb. The H200's 141 GB of HBM3e and high bandwidth directly attack this constraint, letting a single GPU serve larger models and longer contexts before additional GPUs are needed.
02Throughput per dollar
For inference at scale, the metric that matters is cost per token (or per request) at an acceptable latency. By raising the number of concurrent sequences a GPU can hold and serve, the H200 improves throughput per dollar — often the difference between an AI feature that is economically viable and one that is not.
03Designing an inference tier
Getting inference economics right means matching GPU memory to model size, batching intelligently, and provisioning a network that keeps multi-GPU models responsive. Semifly designs inference tiers around real traffic patterns so capacity tracks demand and cost stays under control as usage grows.
← Back to Insights
