CPU vs GPU vs NPU: Which Semiconductor Does What?

Understanding the hardware landscape is critical for local LLM practitioners, and this explainer breaks down the distinct roles of CPUs, GPUs, and the increasingly relevant NPU (Neural Processing Unit) category. NPUs are purpose-built accelerators for AI workloads that offer significant power efficiency advantages over general-purpose GPUs, making them particularly attractive for edge and mobile deployment. While GPUs excel at parallel matrix operations needed for transformer inference, NPUs often deliver better performance-per-watt through specialized instruction sets and lower precision arithmetic.

The emergence of NPUs in consumer devices—including recent smartphone and laptop processors from Qualcomm, Apple, and others—is reshaping the local LLM landscape. Practitioners can no longer assume GPU acceleration is always available or necessary. Understanding NPU capabilities, limitations, and programming models is becoming essential as hardware manufacturers integrate these accelerators more broadly. This knowledge helps inform model selection, quantization strategy, and deployment target decisions.

For local LLM builders, this primer emphasizes that hardware diversity is a feature, not a bug. The ability to run inference across CPUs, GPUs, and NPUs with minimal code changes—through frameworks like ONNX Runtime or specialized quantization tools—is increasingly important. The optimal choice depends on target hardware, latency requirements, power constraints, and model size.

Source: Google News · Relevance: 7/10