On-Device AI

The smartphone in your pocket has more computing power than the entire NASA control room that guided Apollo 11 to the Moon. Yet until 2024, running a useful language model entirely on that device seemed like science fiction. The revolution that made it possible wasn’t a single breakthrough—it was a cascade of engineering innovations that fundamentally rethought how neural networks run on constrained hardware. The Memory Bandwidth Abyss The first and most brutal constraint facing on-device LLMs isn’t compute—it’s data movement. When you run a 7-billion parameter model on an H100 GPU, you’re working with memory bandwidth of 3.35 TB/s. A flagship smartphone in 2026? You get 50-90 GB/s through its LPDDR5X memory. That’s a 30-50x gap, and it dominates every architectural decision. ...

The agentic AI revolution has a dirty secret: it’s burning through compute budgets at an alarming rate. Organizations deploying LLM-powered agents are discovering that their “intelligent” systems are fundamentally inefficient—using sledgehammers to crack nuts. A groundbreaking 2025 NVIDIA Research paper now challenges this paradigm entirely, arguing that small language models (SLMs) are not just viable alternatives but the future of agentic AI. The Efficiency Paradox of Agentic Workloads When we think of AI agents, we imagine systems requiring frontier-level reasoning. Yet the reality of agentic workloads reveals a different picture. Most agent operations are surprisingly narrow: parsing commands, generating structured JSON for tool calls, summarizing documents, answering contextualized queries. These tasks are repetitive, predictable, and highly specialized. ...