Why Is Inference Efficiency Harder Than It Looks?
DDN Blog, Wednesday, June 24th, 2026
DDN argues measuring AI inference efficiency now requires accounting for reasoning depth, disaggregation, and caching.
DDN examines why measuring AI inference efficiency has become far more complex, arguing the old one prompt, one model, one GPU mental model is obsolete.
Chain-of-thought reasoning models like DeepSeek R1 expanded hidden token counts from around 200 to 10,000-20,000 or more per query.
Modern systems disaggregate compute-bound prefill and memory-bound decode phases onto separate GPU pools.
Agentic workloads can reuse 85-99% of KV cache across multi-tier hierarchies.
Reliable efficiency models must account for reasoning depth, disaggregation, compression, heterogeneous hardware, and evolving architectures.