Morning Edition · Monday, August 3, 2026Published at 1:38 AM EDT · New York
A New Paper Names the Networking Bottleneck No Disaggregated Inference System Solves Correctly
When the prefill and decode stages run on separate graphics-processing-unit (GPU) pools, moving the key-value cache between them becomes a data-center data-movement problem, and the authors argue current systems handle it incorrectly.
Disaggregated inference, which splits the compute-bound prefill stage and the memory-bound decode stage onto separate accelerator pools, has become standard practice at serving scale because it lets each stage be provisioned and batched ind…
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Part of a tracked trend
The Inference-Cost Efficiency Race
Techniques that cut tokens generated and KV-cache memory per query will keep compressing the marginal cost of serving reasoning models, making inference efficiency a recurring competitive axis alongside raw capability.
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