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. This parallel architecture differs fundamentally from CPU design, which typically relies on a few powerful cores optimized for sequential instruction execution. The shift from traditional graphics rendering to general-purpose computing on GPUs (GPGPU) has enabled the hardware to accelerate workloads in scientific computing, artificial intelligence, and data analytics.
The physical structure of a GPU consists of multiple streaming multiprocessors, each containing numerous CUDA cores. These cores are organized into clusters that share memory resources, allowing for efficient data exchange during complex computations. Memory bandwidth and capacity are critical components of this architecture, as they determine how quickly data can be moved between the processor and the video random-access memory (VRAM). High-capacity VRAM is essential for storing large datasets and model parameters without relying on slower system memory.
In the context of large language models, GPU architecture directly influences inference and training capabilities. Devices with significant VRAM capacity, such as NVIDIA GPUs with 48GB of memory, can accommodate quantized versions of large models like Llama 3.1 70B, Gemma 2 27B, Qwen 2 72B, and Mistral Large. The ability to fit these models into VRAM reduces latency and improves throughput by minimizing data transfer bottlenecks between the CPU and GPU. Consequently, architectural advancements in memory hierarchy and core density are key drivers in the deployment of complex AI applications.