What is Hardware acceleration, GPUs, NPUs in AI

In this enlightening lecture, Mr. Gyula Rabai explains the critical role of Graphics Processing Units (GPUs) in both gaming and modern AI applications. From video games to large language models (LLMs), GPUs are indispensable for handling complex computations at incredible speeds. If you're curious about how GPUs work, their evolution, and how they accelerate processing in both gaming and AI, this lecture is for you.

What is a GPU?

GPU stands for Graphics Processing Unit, and it was originally developed to handle the complex graphics required for video games. When playing a game, there are intricate visual elements like trees, non-playable characters, and environments that need to be rendered quickly and efficiently. GPUs excel at processing large amounts of graphical data simultaneously, allowing them to display detailed graphics without lag.

How Do GPUs Work?

A GPU processes vertices, which are individual points used to create complex 3D objects, and each vertex is typically represented by three numbers (X, Y, Z coordinates). Unlike a CPU (Central Processing Unit), which processes one task at a time, a GPU has thousands of Number Processing Units (NPUs) that can process multiple sets of numbers simultaneously, enabling faster and more efficient rendering of complex scenes.

The Role of GPUs in AI and Language Models

While GPUs were originally used for gaming, their parallel processing capabilities are also critical in AI applications, particularly in training and running large language models (LLMs). These models, which can contain up to 70 billion parameters, require massive computational power to process vast amounts of data at once. A GPU can handle batches of numbers simultaneously, drastically speeding up the computations needed to predict the next word in a sequence, making them essential for AI tasks.

Key Takeaways:

• GPUs enable faster processing by handling multiple numbers at once, which is crucial in both gaming and AI applications.
• The evolution of GPUs has made them indispensable for rendering complex 3D graphics in games and processing billions of parameters in AI models.
• GPUs are essential for hardware acceleration, improving speed and efficiency in large-scale AI tasks like running language models.

Conclusion

Mr. Rabai’s clear and insightful explanation of GPUs helps you understand their vital role in shaping the future of both gaming and AI. Whether you're interested in gaming technology or AI advancements, this lecture will give you a deeper understanding of how GPUs make high-speed processing possible.

More information

• Large Language Models (LLM) - What is AI
• Large Language Models (LLM) - What are LLMs
• Large Language Models (LLM) - Tokenization in AI
• Large Language Models (LLM) - Embedding in AI
• Large Language Models (LLM) - RoPE (Positional Encoding) in AI
• Large Language Models (LLM) - Layers in AI
• Large Language Models (LLM) - Attention in AI
• Large Language Models (LLM) - GLU (Gated Liner Unit) in AI
• Large Language Models (LLMs) - Normalization (RMS or RMSNorm) in AI
• Large Language Models (LLM) - Unembedding in AI
• Large Language Models (LLM) - Temperature in AI
• Large Language Models (LLM) - Model size an Parameter size in AI
• Large Language Models (LLM) - Training in AI
• Large Language Models (LLM) - Hardware acceleration, GPUs, NPUs in AI
• Large Language Models (LLM) - Templates in AI
• Large Language Models (LLM) - Putting it all together - The Architecture of LLama3