GPU Architecture
GPU architecture refers to the design and structure of graphics processing units, which are specialized computing devices optimized for parallel processing tasks. Modern GPUs, particularly those manufactured by NVIDIA, contain thousands of small cores designed to handle multiple operations simultaneously, making them well-suited for computationally intensive workloads beyond traditional graphics rendering. This parallel architecture differs fundamentally from CPU design, which prioritizes sequential execution and low-latency computation on a smaller number of cores.
Memory and Computational Capacity
Contemporary high-end GPUs typically feature 24GB to 48GB of VRAM, with 48GB models enabling the execution of quantized large language models such as Llama 3.1 70B, Gemma 2 27B, Qwen 2 72B, and Mistral Large. Quantization reduces model size and memory requirements by representing weights and activations with lower precision, allowing models that would otherwise require 140GB or more of memory to run on consumer-grade hardware. The memory bandwidth of modern GPUs—often exceeding 500GB/s—supports the high-throughput data movement required by these models during inference.
Application in Machine Learning
GPUs have become essential infrastructure for machine learning workflows, from model training to inference. Their parallel architecture naturally maps to the matrix operations and tensor computations that form the basis of neural network processing. The ability to run large models on single high-capacity GPUs has democratized access to advanced language models, enabling researchers and practitioners to deploy sophisticated AI systems without requiring specialized data center environments.