// edge computing

Source: The Register: Biting the hand that feeds

As cloud-based LLM inference costs mount—particularly for enterprises running high-frequency queries—Gartner is forecasting a shift toward on-device processing, where corporations route routine tasks to local AI PCs rather than continuous API calls to providers like OpenAI or Anthropic. A $2,000 machine amortized over three years becomes cheaper than paying per-token for tasks that don't require frontier models. Chipmakers (Intel, AMD, Nvidia) and PC makers benefit from the refresh cycle acceleration, while API providers face pressure to cut margins or concentrate on tasks where cloud still makes economic sense.

Source: SiliconANGLE

As large language model inference becomes prohibitively expensive at cloud scale—particularly for always-on agentic workloads that generate token after token—enterprises are reconsidering local compute as the economically rational choice rather than a technical compromise. This reversal hinges on a specific technical arbitrage: running smaller, quantized models on corporate desktops and edge devices eliminates per-token billing while keeping sensitive data off third-party infrastructure, a calculation that flips when cloud providers charge $0.10+ per million input tokens. The shift doesn't mean abandoning cloud entirely, but rather treating it as a premium option for complex reasoning rather than the default for routine tasks—changing the infrastructure economics that have dominated the past five years.