Tech About PH

Agent adoption is exploding and in most cases agents run inference in the cloud. The implications are clear: rising Cloud API costs, sensitive data leaves the device, and the workflow only runs when connected to the network. Running inference locally flips the equation: no per-token cloud cost, data that never leaves the machine, offline and low-latency operation, and full control over the model and the agent. The question is: Can you actually run a realistic agentic workload locally. Previously, we showed that even when inference runs in the cloud, the most important upgrade might be the CPU in your system because the agent loop (planning, routing, moving data, assembling results) is orchestration, and orchestration is CPU work that runs locally no matter where the tokens are generated. Here, we complete the story for on-device inference. When the whole agent runs locally, the chat-era instinct is to ask which system has the better GPU. But agent workflows aren’t one large matrix mul...