GPU (Graphics Processing Unit)
Definition
A GPU is a specialized processor designed for parallel computation, essential for training and running AI models due to its ability to perform thousands of matrix operations simultaneously.
Why It Matters
GPUs transformed AI from academic curiosity to practical technology. Neural networks are essentially massive matrix multiplications, operations that CPUs execute sequentially but GPUs parallelize across thousands of cores. This parallelization makes training that would take weeks on CPUs complete in hours on GPUs.
For AI engineers, understanding GPU capabilities determines what you can build and how fast. LLM inference speed depends heavily on GPU memory bandwidth and VRAM capacity. A model that takes 10 seconds per response on CPU might respond in 200 milliseconds on GPU, the difference between unusable and production-ready.
VRAM (Video RAM) is often the limiting factor. Large language models require their weights loaded into GPU memory. A 7B parameter model in 16-bit precision needs approximately 14GB VRAM. Understanding these constraints helps you choose appropriate model sizes and quantization strategies for your hardware.
Implementation Basics
NVIDIA dominates AI GPUs due to CUDA’s extensive framework support. Key specifications to understand:
- VRAM capacity determines the largest model you can load (4GB, 8GB, 16GB, 24GB, 48GB, 80GB tiers)
- Memory bandwidth affects inference speed more than compute for LLMs
- Tensor Cores accelerate matrix operations in specific precisions (FP16, BF16, INT8)
Consumer vs. data center GPUs:
Consumer cards (RTX 3090, 4090) offer good VRAM at lower prices but lack enterprise features. Data center GPUs (A100, H100) provide more VRAM, better memory bandwidth, and multi-GPU interconnects.
GPU memory management for AI:
- Models must fit in VRAM to run efficiently (CPU offloading kills performance)
- Batch size trades throughput for memory, as larger batches use more VRAM
- Quantization (INT8, INT4) reduces memory requirements by 2-4x with acceptable accuracy loss
- Model sharding splits models across multiple GPUs for very large models
When selecting GPUs for AI workloads, prioritize VRAM for LLM inference and training. For production deployments, consider total cost of ownership including power consumption, cooling, and cloud rental costs versus purchase.
Source
GPUs accelerate deep learning by performing massive amounts of matrix math operations in parallel, enabling training of complex neural networks orders of magnitude faster than CPUs.
https://developer.nvidia.com/deep-learning