Serving AI Agents Becomes a Storage and Networking Bottleneck, Starving GPUs
AccBalanced · x · 2026-07-31
As AI agents scale, the performance bottleneck for serving infrastructure has shifted heavily towards storage and networking. Between the Ampere and Blackwell architectures, the ratio of I/O to compute dropped by a factor of 14.4 because FLOPS grew significantly faster than network bandwidth and memory capacity.
Agent workloads, such as coding tasks averaging 157 rounds with 32.7k tokens of context, generate massive prefix caches. This shifts the workload from computing new states to retrieving stored KV states, bottlenecked by a single storage NIC per node and leaving GPUs underutilized.
NVIDIA's response is CMX, a specification that extends caching into NVMe and retains it post-session. Ten storage vendors have already adopted this specification.
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