Károly Zsolnai-Fehér describes how moving data can constrain AI inferenceAI compute infrastructure is the hardware, facilities, networks, power, cooling, storage, and software used to train and run AI models. even when compute capacity is available. He uses a reading analogy to explain why long, multi-turn agentic workloads can be especially demanding.
He describes a DeepSeek approach that uses otherwise available network paths and prioritizes compute traffic over memory transfers. He reports the paper’s utilization comparisonResource utilization measures how much of an available computing resource is being used over a defined period. It can describe processor activity, memory occupancy or other capacity relevant to an AI workload. and emphasizes that the improvement is situational rather than a universal doubling of speed.
The discussion focuses on infrastructure for serving models, not a new model release. The closing sponsor message is excluded from the editorial account.
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