How did graphics cards move from gaming to artificial intelligence ?

1. Introduction

1.1. The Evolution of Graphics Cards Beyond Gaming

1.2. In

1.3. Central Idea:

GPUs speed up AI with parallel processing, revolutionizing computation.

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2. The Origins of GPUs in Gaming

2.1. Early GPU Development and the Rise of 3D Graphics

2.2. How Gaming Demands Pushed Parallel Architecture

2.3. Key Milestones: GeForce, Radeon, and Console GPUs

2.4. Laying the Foundation for AI Applications

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3. Understanding Parallel Processing and Its Importance

3.1. CPU vs GPU Architecture

3.2. Thousands of Cores for Simultaneous Calculations

3.3. Matrix Multiplications and Neural Networks

3.4. Why AI Training Requires Parallelism

3.5. Reinforcing the Keyword:

GPUs speed up AI with parallel processing.

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4. The Transition From Gaming to AI

4.1. Early AI Experiments on GPUs

4.2. Deep Learning Breakthroughs That Needed GPU Power

4.3. After

4.4. How Researchers Realized GPUs Could Outperform CPUs in AI

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5. NVI

5.1. MISCELLANEOUS

5.2. Volta, Ampere, and Hopper: GPUs Built for AI

5.3. AI Data Centers and Cloud Computing

5.4. How NVIDIA Became Synonymous With AI Hardware

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6. Other GPU Players in AI

6.1. AMD and Radeon AI Accelerators

6.2. Intel’s GPUs and AI Efforts

6.3. Specialized AI Chips vs General-Purpose GPUs

6.4. Market Competition Driving Innovation

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7. Real-World Applications of GPUs in AI

7.1. The

7.2. Autonomous Vehicles and Robotics

7.3. Healthcare: Medical Imaging and Diagnostics

7.4. Finance: Predictive Analytics and Fraud Detection

7.5. Scientific Simulations and Supercomputing

7.6. AI Acceleration Across Industries:

GPU

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8. No

8.1. Power Consumption and Thermal Management

8.2. GPU Shortages and Supply Chain Issues

8.3. Scalability Limits of Parallel Processing

8

8.5. Was

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9. The Future of GPUs in AI

9.1. Next-Generation GPU Architectures

9.2. Integration With Custom AI Accelerators

9.3. Cloud AI and Distributed GPU Clusters

9.4. After

9.5. Continued Role of GPUs:

GPUs speed up AI with parallel processing.