ModelQuantization

Introduction In production AI, latency is often the make‑or‑break metric. A self‑driving car cannot wait 100 ms for a perception model, a voice‑assistant must respond within a few hundred milliseconds, and high‑frequency trading systems demand micro‑second decisions. While modern GPUs can deliver massive FLOPs, raw compute power alone does not guarantee low latency. The architecture of the inference pipeline, the precision of the model, and the runtime optimizations all interact to determine the end‑to‑end response time. ...

Introduction Large language models (LLMs) have captured headlines for their ability to generate human‑like text, answer questions, and even write code. Yet the majority of these breakthroughs rely on massive cloud‑based clusters equipped with dozens of GPUs and terabytes of memory. For many applications—smartphones, IoT sensors, industrial controllers, and autonomous drones—sending data to a remote server is undesirable due to latency, privacy, connectivity, or cost constraints. Enter local LLMs: compact, purpose‑built language models that can run directly on edge devices. Over the past two years, a confluence of research breakthroughs, tooling improvements, and hardware advances has made it feasible to run inference for models as small as 1 B parameters on a modest ARM CPU, or even sub‑100 M‑parameter models on microcontrollers. This blog post provides a deep dive into why local LLMs are rising, how they are optimized for edge inference, and what practical steps developers can take today. ...