Running AI Models Locally on a CPU Is More Viable Than You Think

Contrary to popular belief, running large language models locally does not always require an expensive GPU, as inference workloads are primarily memory-bandwidth bound rather than compute bound. A practical demonstration showed a 7-billion-parameter model running at 10–12 tokens per second on a three-year-old office laptop with 16 GB RAM and no discrete GPU. Benchmarks across consumer hardware show speeds ranging from around 10 tokens per second on older laptops to 25–35 tokens per second on Apple M2/M3 MacBooks, with comfortable reading speed sitting at roughly 20 tokens per second. Modern CPUs with integrated neural processing units (NPUs) from Intel, AMD, and Qualcomm can further improve performance while reducing power consumption. The approach is best suited for single-user, private workloads such as document assistants, while high-concurrency or latency-sensitive applications still benefit significantly from dedicated GPU hardware.