Inference Economics With Long Context: KV Cache, Batching, and Cost per Task
Long context is powerful, but expensive. Understanding KV cache growth, batching limits, and prompt design is the fastest path to cutting cost without cutting quality.
Why long context hurts
The memory footprint of KV cache grows roughly linearly with context length and layers. That makes long prompts an immediate tax on throughput.
If you scale context without rethinking architecture, you’ll pay with tail latency and GPU memory fragmentation.
Batching isn’t free
Batching improves utilization, but long contexts reduce the number of concurrent sequences that fit in memory. This is why naive “just batch more” advice stops working.
A pragmatic solution is hybrid batching: short requests in high-throughput lanes, long requests in a dedicated lane with stricter limits.
Prompt design as cost control
The biggest wins often come from reducing prompt size: summarize chat history, retrieve only relevant chunks, and avoid repeating tool results.
Use structured context: headings, bullet points, and stable templates reduce token bloat and improve model attention.