Rajeev Gadgil

JetStream 3 deliberately moved away from microbenchmarks, and for browser engineers that was the right call. Small, tight loops once let engines win benchmarks without making real web apps faster. Pre-silicon CPU teams face the opposite problem. They need to know how a chip that doesn't exist yet will run browser workloads, and a full JavaScript suite is nearly impossible to run on a simulator. The answer is not to abandon microbenchmarks, but to build ones that are correlated with workloads...

Every silicon vendor at every keynote this year has said some version of the same sentence: the next CPU generation will be built for agentic workloads. Intel talked about CPUs as the orchestration layer for agentic systems, Arm argued that agents are driving a fresh wave of data centre CPU demand, and NVIDIA introduced Vera as a CPU designed for agentic workflows rather than general-purpose compute. The direction is settled. The definition is not. Because here is the question nobody on those...

Agentic AI has done something no previous AI workload managed: it made the host CPU interesting again. In single-shot inference the CPU tokenizes a prompt, hands off to the accelerator, and waits. In an agent loop it runs everything between model calls — orchestration and control flow, tool dispatch, sandbox spawn and teardown, file I/O, test execution, retrieval, JSON parsing, retries. Recent characterization work has put numbers on this. In tool-dominated agentic pipelines, CPU-side tool...