GPU (Graphics Processing Unit)
Specialized processors designed for parallel computation that power modern AI training and inference
What is a GPU?
A Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and perform parallel computations. Originally created for rendering graphics in video games, GPUs have become the backbone of modern artificial intelligence due to their ability to perform thousands of calculations simultaneously.
Think of a GPU as having thousands of small, efficient workers compared to a CPU's few powerful managers. While a CPU might have 8-32 cores optimized for sequential processing, a modern GPU has thousands of smaller cores designed for parallel tasks. This architecture makes GPUs ideal for the matrix operations and parallel computations that drive neural network training and inference.
GPUs are the foundation of the AI revolution, powering everything from training foundation models like Claude 4 and GPT-4 to running real-time inference in production systems. NVIDIA's dominance in this space has made them one of the world's most valuable companies, with their chips becoming the "oil" of the AI economy.
How GPUs Power AI
Parallel Processing Architecture
GPUs contain thousands of cores that can execute operations simultaneously, perfectly suited for the matrix multiplications and parallel computations that form the foundation of neural networks.
High Memory Bandwidth
Modern AI GPUs feature high-bandwidth memory (HBM) that can transfer data at speeds of over 3TB/s, essential for feeding data to thousands of processing cores efficiently.
Tensor Operations
Specialized tensor cores accelerate the mixed-precision operations common in deep learning, providing massive speedups for training and inference workloads.
CUDA Ecosystem
NVIDIA's CUDA platform provides the software foundation that makes GPUs programmable for general computing tasks, creating a massive ecosystem of AI tools and frameworks.
CPU vs GPU Performance
NVIDIA's AI GPU Leadership (2025)
H100 Data Center GPU
- Memory 80GB HBM3
- Memory Bandwidth 3.35 TB/s
- FP16 Performance 1,979 TFLOPS
- Price $25,000-40,000
B200 Blackwell GPU
- Memory 192GB HBM3e
- Memory Bandwidth 8 TB/s
- FP4 Performance 20,000 TFLOPS
- Status Next Generation
Market Dominance
- AI GPU Market Share 95%+
- Data Center Revenue $60B+ annually
- Major Customers Meta, Google, OpenAI
- Backlog Months-long waits
Competitive Landscape
- AMD MI300X Alternative Option
- Intel Gaudi Enterprise Focus
- Google TPU Google Cloud Only
- Custom Silicon Tech Giants
Business Applications
AI Model Training
Large-scale AI model training requires massive GPU clusters, with foundation models like GPT-4 trained on systems with 10,000+ GPUs running for months.
Real-Time AI Inference
Production AI applications use GPU clusters to serve millions of users simultaneously, powering chatbots, recommendation engines, and real-time analytics.
Scientific Computing
GPUs accelerate research in climate modeling, drug discovery, financial modeling, and scientific simulations by orders of magnitude.
Edge AI Processing
Smaller GPU chips enable AI processing in autonomous vehicles, robots, smart cameras, and IoT devices for real-time decision making.
Creative and Media Production
GPUs power AI-driven content creation, video editing, 3D rendering, and real-time graphics generation for entertainment and marketing.
GPU Economics & Strategic Importance
Supply Chain Bottlenecks
Limited TSMC fab capacity and complex manufacturing create severe GPU shortages, with wait times extending months and driving up prices significantly.
Geopolitical Implications
GPU access is becoming a national security issue, with export controls limiting China's access to advanced chips and driving technological competition.
Cloud vs On-Premises
Organizations choose between expensive on-premises GPU clusters ($millions) or cloud GPU services with usage-based pricing but less control.
Investment Requirements
AI companies require massive capital for GPU infrastructure, with leading startups raising hundreds of millions primarily for compute resources.