Next-Gen GPU Computing
Architecture Education
Explore next-generation GPU accelerator architecture with HBM3e memory and high-speed interconnect fabric. Learn about AI compute infrastructure concepts through 6 interactive experiences.
Introduction to Next-Gen GPU Computing
Understanding the fundamentals of modern GPU accelerator architecture and AI compute infrastructure.
What is GPU Computing?
Graphics Processing Units (GPUs) have evolved beyond rendering graphics into massively parallel compute accelerators. Modern AI workloads require processing thousands of operations simultaneously—a perfect match for GPU architecture with thousands of cores.
Unlike CPUs which optimize for sequential tasks, GPUs excel at parallel matrix operations that dominate neural network training and inference.
The AI Acceleration Revolution
Training large language models (LLMs) like GPT-4 requires exaFLOPS of compute. A single modern GPU can deliver 20 petaFLOPS of FP8 performance—enabling models with hundreds of billions of parameters.
Next-generation architectures combine specialized tensor cores, high-bandwidth memory, and multi-GPU interconnects to handle today's demanding AI workloads.
Key Performance Metrics
Why This Matters for AI
Transformer Models Dominate: Modern LLMs rely on self-attention mechanisms that are matrix multiplication intensive—GPUs accelerate this by 100x over CPUs.
Memory is the Bottleneck: Loading a 70B parameter model at FP16 requires 140GB. High-bandwidth memory reduces this latency from seconds to milliseconds.
Scale Requires Parallelism: Training GPT-class models needs thousands of GPUs working in parallel. Fast interconnects enable efficient gradient synchronization.
Explore Next-Gen GPU Architecture
This is an educational demonstration of next-generation GPU compute concepts.