Agentic AI · Nvidia · Amazon · Llama · Intel · Strategy · IEEE Spectrum AI
It’s conceivable that future tokenizers will detect ways to mitigate this, Chung confirms
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The paper finds that time-to-first-token latency (the time required for the model to produce the first word of its reply) can increase dramatically as the sequence length grows.
Key facts
- In test runs at longer sequence lengths, increasing CPU core counts can reduce time-to-first-token latency by roughly 1.5x to 7x
- Matt Kimball, vice president and principal datacenter analyst at Moor Insights & Strategy, says 2026 has brought a spike in CPU demand, much of it due to agentic AI
- Even Nvidia has prioritized Vera, its Arm-based CPU for agentic AI, which is part of Nvidia’s Vera Rubin platform
- If you have an ongoing sequence of, say, 100,000 tokens, and you have a tool result of a 1,000 tokens, the tokenizer will have to tokenize the whole sequence again
Summary
Smith is a contributing editor for IEEE Spectrum and the former lead reviews editor at Digital Trends. Earlier this year, leaders at Amazon Web Services delivered a new mandate to their engineers: they need to conserve CPU cycles at all costs. The issue seemingly took AWS off-guard, and for good reason. But the rise of agentic AI systems, which allow AI models to operate autonomously and call on sub-agents, is changing the narrative. Matt Kimball, vice president and principal datacenter analyst at Moor Insights & Strategy, says 2026 has brought a spike in CPU demand, much of it due to agentic AI.