A GPU is the muscle behind much of modern AI. Large language models need enormous amounts of computation, and cheap, widely available GPUs helped make training and deploying early LLMs practical rather than a science-fiction procurement nightmare. section 1.8
The useful distinction is workload, not mystique. CPUs can struggle with the massive computational demands of AI algorithms, while GPUs are specialized accelerators that can run parallel calculations efficiently. That makes them well suited to matrix calculations, neural-network operations, and other compute-heavy AI work. section 10.8
GPU acceleration can improve AI systems in several practical ways:
- Speed up model training and inference, supporting real-time or near-real-time applications. section 10.8
- Improve energy efficiency and potentially reduce operational costs for large-scale AI deployment. section 10.8
- Provide a cloud alternative when local hardware cannot supply enough processing power or dedicated video RAM. section 9.2
Choosing hardware for a local LLM
Before downloading a model, check whether your machine can actually run it. Many models rely on GPUs and may need substantial dedicated video RAM. A consumer laptop or desktop may require a CPU-only model instead; if local hardware is not enough, rent GPU capacity from a cloud provider. Fancy model, humble laptop: an awkward pairing worth noticing before the download finishes. section 9.2
GPUs are part of a broader acceleration toolbox. TPUs, FPGAs, and ASICs can also target AI workloads, while parallel processing, distributed computing, and model compression improve computational efficiency in other ways. The durable lesson is to match the model and workload to available hardware, then verify the practical requirements before committing to a deployment plan. section 1.8 section 10.8
