Perplexity has brought its Portable Computer agent to Windows, where it runs models locally on the device rather than in the cloud. The tool is a local variant of the company's Perplexity Computer agent, which decomposes and executes multi-step tasks on its own. NVIDIA RTX GPUs provide the acceleration, and keeping processing on the machine means sensitive data does not leave the PC.
A new measurement effort proposes comparing local AI models by how much useful capability they deliver for each unit of energy they consume, rather than by raw benchmark scores alone. The work focuses on models run on local hardware, where power draw and thermal limits directly shape practical performance. It offers a way to weigh efficiency alongside accuracy when choosing or deploying smaller models.
Hugging Face introduced a collection of more than 200 WebGPU kernels intended to speed up AI workloads running locally in the browser or on user devices. The library is published under the huggingface/kernels namespace, giving developers reusable GPU-accelerated operations for client-side inference. It targets the growing interest in running models without sending data to remote servers.
WHY IT MATTERS ↘Reusable, standardized WebGPU kernels lower the engineering cost of client-side inference, making browser and on-device deployment viable for more teams without custom GPU work. That shifts some inference demand away from cloud APIs toward local hardware, weakening vendor lock-in but also complicating model governance since data and weights increasingly live outside the provider's control.