What is GPU?
A GPU (Graphics Processing Unit) is a specialized processor originally designed for rendering graphics that has become the dominant hardware for training and running AI models. Its architecture of thousands of small parallel cores makes it exceptionally efficient at the matrix operations that power deep learning.
GPU Explained
GPUs are the hardware backbone of the AI revolution. When researchers discovered in the early 2010s that GPUs could accelerate deep learning training by orders of magnitude compared to CPUs, it triggered a cascade of breakthroughs that continues today. The reason GPUs are so effective for AI is architectural: while a CPU has a small number of powerful cores optimized for sequential tasks, a GPU has thousands of smaller cores designed to perform many simple calculations simultaneously. Matrix multiplication, the fundamental operation in neural networks, maps perfectly onto this parallel architecture.
Training large AI models requires vast amounts of GPU compute. Training a frontier large language model today requires thousands of high-end GPUs running for weeks or months, consuming megawatts of power and costing tens to hundreds of millions of dollars. This concentration of required compute is one reason why only a handful of organizations can train frontier models from scratch. The democratization of AI applications is only possible because trained models can be served via APIs and cloud AI platforms without each user needing their own GPU cluster.
For inference, GPU requirements are substantially lower than for training, though still significant at scale. Techniques like quantization, which reduces the numerical precision of model weights, and batching, which processes multiple requests together, improve GPU utilization efficiency. Small language models are partly attractive because they can perform inference on consumer-grade GPUs or even without GPUs entirely, enabling edge AI deployments on laptops and mobile devices.
The GPU supply chain has become a geopolitical issue as demand for AI compute has outstripped supply. NVIDIA dominates the AI GPU market, with its H100 and successor chips becoming the essential infrastructure of AI development. Alternative approaches including TPUs, custom AI accelerators from major cloud providers, and novel chip architectures are all competing to reduce dependence on a single supplier and improve the economics of AI compute at scale.
Key Takeaways
Where is GPU Used?
Training large AI models, running inference at scale, computer vision, scientific computing, and high-performance AI research.
How Copilotly Uses GPU
Every answer a Copilotly user receives is computed on GPU clusters in the cloud, which is what makes the service feel instant inside a browser sidebar. Because GPU time is the dominant cost of serving AI, Copilotly routes lightweight requests, like a quick grammar fix from the Writing Copilot, differently from heavy research tasks to keep responses fast and affordable.
Get Your Answer Now, Free
See gpu in action with Copilotly's specialized AI copilots.
Frequently Asked Questions
What is the difference between a GPU and a TPU?+
A GPU is a general-purpose parallel processor, originally built for graphics, that excels at the matrix operations in deep learning. A TPU is Google's custom chip designed exclusively for tensor math in neural networks. TPUs can be more efficient for specific large-scale workloads, while GPUs offer broader software support and availability.
Why are GPUs better than CPUs for AI?+
A CPU has a handful of powerful cores optimized for sequential tasks, while a GPU packs thousands of smaller cores that run the same operation on many data points simultaneously. Neural network training is mostly parallel matrix multiplication, so GPUs complete it tens to hundreds of times faster.
Do you need a GPU to use AI tools?+
Not as an end user. Services like ChatGPT and Copilotly run inference on GPUs in cloud data centers, so any laptop or phone can access them through a browser. You only need local GPU hardware if you are training models yourself or running large models on-device.
How much GPU memory does running an LLM require?+
A rough rule is two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs about 14 GB of VRAM, before accounting for the context cache. Quantization to 4-bit can cut that to roughly 4-5 GB, which is why compressed open models can run on consumer cards.
Get AI Help Right Where You Browse
Use Copilotly's Get AI-powered professional guidance on any webpage. 131 specialized copilots. copilot directly on any webpage. No tab switching.