The shape of compute
Throughput, latency, memory, and the architecture of useful intelligence.
Under the abstraction.
Not one resource
Compute is often discussed as a single quantity. In a real system, the shape of the workload matters. A task can be limited by memory, communication, latency, or the availability of parallel work.
This is why a comparison that looks clear in one benchmark may tell us little about a different application. The workload and the surrounding system belong in the same frame.
From capability to experience
Users experience waiting, reliability, and the ability to complete a task. They do not experience a chip specification in isolation. The engineering challenge is to connect those human outcomes to the resources underneath.
That connection creates room for different approaches: a new architecture, a better scheduling policy, a smaller model, or a more carefully designed interaction.
Look across the stack
An improvement at one layer can change what is sensible at another. The most useful research crosses those boundaries and asks where a local optimization becomes a system-level advantage.
That is the thread worth following: not simply more compute, but a better fit between a resource and the work it is meant to do.
This is an illustrative essay written for the website design preview.
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