Why GPUs Matter for AI
- Deep learning = massive matrix/tensor operations
- Requires highly parallel compute
- GPUs provide thousands of cores, high memory bandwidth, and AI accelerators
- Backbone of modern AI training and inference
CPUs Are Not Enough
- Optimized for sequential, general-purpose workloads
- 4–64 powerful cores but limited parallelism
- Inefficient for large-scale matrix multiplications
How GPUs Accelerate Matrix Operations
- Thousands of lightweight cores for massive parallelism
- High arithmetic throughput for linear algebra
- Significantly faster training and inference
- Reduces time-to-result for deep learning workloads
GPU Bandwidth Enables Fast Inference
- High-speed GDDR7 Memory (e.g., up to 1.8 TB/s on RTX Pro 6000 Blackwell Server edition)
- High-bandwidth HBM (e.g., up to 3 TB/s on H100)
- Rapid weight/activation access for low-latency inference
- High throughput per watt and per dollar
- Efficient batching for real-time and large-scale deployments
GPUs vs TPUs vs NPUs
