From Gaming to AI: Understanding GPUs

 

From Gaming to AI: Understanding GPUs

A Crisp UPSC-Oriented Explainer

The journey of the Graphics Processing Unit (GPU) — from accelerating videogames to powering artificial intelligence — is a story of how computing architecture shapes the digital economy. For UPSC aspirants, this topic links science & technology, economy, energy, and geopolitics.

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🧠 What Exactly Is a GPU?

A GPU is a processor designed for:

✅ Performing many simple calculations simultaneously

✅ Handling massively parallel workloads

⚙️ GPU vs CPU

Feature	CPU	GPU
Strength	Complex tasks	Parallel tasks
Cores	Few, powerful	Thousands, simpler
Ideal for	Logic, decision-making	Repetitive math

Analogy:

CPU → Expert problem-solver

GPU → Army of fast workers

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🖥️ Why GPUs Excel at Graphics

Rendering an image requires:

• Updating millions of pixels per frame

• Repeating identical calculations

Example:

📺 1920×1080 screen = 2.07 million pixels/frame

🎞️ 60 FPS → 120+ million pixel updates/sec

Perfect for GPU’s parallel design.

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🔄 How Does a GPU Render Images?

🎨 Rendering Pipeline

1️⃣ Vertex Processing → Position triangles

2️⃣ Rasterisation → Convert shapes → pixels

3️⃣ Fragment Shading → Compute colours, lighting

4️⃣ Frame Buffer Write → Final image

👉 All powered by tiny programs called shaders

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💾 Why GPUs Need VRAM

GPUs move enormous data:

• 3D models

• Textures

• Pixel data

Thus they use VRAM (Video RAM):

✔ High bandwidth

✔ Faster data movement

✔ Reduces bottlenecks

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📍 Where Is the GPU Located?

Two possibilities:

🖥️ Discrete GPU

• Separate graphics card

• Own VRAM

• High performance

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💻 Integrated GPU

• On same die as CPU

• Common in laptops/smartphones

• Energy efficient

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🔬 Are GPUs “Smaller” Than CPUs?

❌ No — same silicon tech

Difference lies in:

• Microarchitecture

• GPU → More compute blocks

• CPU → More control logic & cache

👉 High-end GPUs are often larger than CPUs

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🤖 Why Neural Networks Love GPUs

Neural networks rely on:

✔ Matrix multiplications

✔ Tensor operations

✔ Repetitive arithmetic

Perfect match for:

✅ Thousands of GPU cores

✅ Tensor cores (special units)

✅ High memory bandwidth

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⚡ GPU Performance Example

Advanced GPUs (e.g., H100-class):

• ~ Quadrillions of operations/sec

• Optimised for AI workloads

Also note:

👉 Google’s TPUs = AI-specific chips

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🔋 Energy Consumption Considerations

Example scenario:

• 4 GPUs × 250W

• 12-hour training

Energy ≈ 12 kWh (training)

Add:

✔ CPU

✔ Cooling

✔ Networking

➡️ Real-world usage ↑ by 30–60%

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🌍 Why Energy Use Matters (UPSC Link)

Relevant to:

• Climate change debates

• Data centre sustainability

• AI carbon footprint

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🏭 Does Nvidia Have a Monopoly?

❌ Technically no

✅ Practically dominant

📊 Market Position

Segment	Nvidia Share
Discrete GPUs	~90%
Data Centre AI	Extremely strong

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🧩 CUDA Advantage

CUDA = Nvidia’s proprietary ecosystem

✔ Developers build software around it

✔ Switching costs high

✔ Creates vendor lock-in

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⚖️ Regulatory Concerns

Investigations examine:

• Anti-competitive practices

• Bundling hardware + software

• Market dominance abuse

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📝 UPSC Exam Relevance

Prelims

• CPU vs GPU difference

• VRAM purpose

• CUDA / TPU basics

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GS Paper III

• AI infrastructure

• Semiconductor ecosystem

• Energy implications

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Essay Ideas

• Compute Power as the New Oil

• AI, Energy & Sustainability

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✅ Key Takeaway for Aspirants

✔ Understand fundamentals (parallelism)

✔ Link tech → economy → geopolitics

✔ Recognise energy & policy angles

✔ Avoid hype-driven answers