Am I missing something or if you don’t use words loosely and clickbaitly, inference doesn’t have anything to do with CPU? All the tools and orchestration examples are not actually part of the inference. Honestly, this article is 90% fluff trying to justify misleading title
> What's changed is the perimeter of that statement. Agentic AI introduced a class of workloads—orchestration, tool execution, code sandboxing, and multi-agent coordination—where CPUs are the correct tool for the job.
What exactly are they saying here or what is it supposed to mean? What would you use for "tool execution, code sandboxing, and multi-agent coordination" if not a CPU? The GPU?
Edit: Skimming more it's becoming painfully obvious this is AI slop and I fell for it yet again, guess it's just hallucinated Claude (IBM Granite?) drivel.
Slop language aside, the abstract insight is directionally correct I think.
CPUs were already becoming much more important during training for test-time scaling, but there you were still bottlenecked by GPU compute since the gradient updates back to the policy model are the actual gating factor.
During normal inference though, CPUs are becoming more of a bottleneck for more advanced workloads. Even if you have 20 agents running in parallel, if they're all compiling Rust concurrently your total wall-clock time per task is no longer bound by the decode throughput of the upstream model. You're just waiting for tools to execute. This gets compounded by VM/container overhead as well if you're doing the totally local sandbox approach.
Claude, write a punchy blog post that inappropriately manipulates the definition of “inference” to make intel feel less threatened.
Am I missing something or if you don’t use words loosely and clickbaitly, inference doesn’t have anything to do with CPU? All the tools and orchestration examples are not actually part of the inference. Honestly, this article is 90% fluff trying to justify misleading title
> What's changed is the perimeter of that statement. Agentic AI introduced a class of workloads—orchestration, tool execution, code sandboxing, and multi-agent coordination—where CPUs are the correct tool for the job.
What exactly are they saying here or what is it supposed to mean? What would you use for "tool execution, code sandboxing, and multi-agent coordination" if not a CPU? The GPU?
Edit: Skimming more it's becoming painfully obvious this is AI slop and I fell for it yet again, guess it's just hallucinated Claude (IBM Granite?) drivel.
Slop language aside, the abstract insight is directionally correct I think.
CPUs were already becoming much more important during training for test-time scaling, but there you were still bottlenecked by GPU compute since the gradient updates back to the policy model are the actual gating factor.
During normal inference though, CPUs are becoming more of a bottleneck for more advanced workloads. Even if you have 20 agents running in parallel, if they're all compiling Rust concurrently your total wall-clock time per task is no longer bound by the decode throughput of the upstream model. You're just waiting for tools to execute. This gets compounded by VM/container overhead as well if you're doing the totally local sandbox approach.
It's been said before that the best human programmers were always bottlenecked by compile test loop time