AiVibeGraphics Processing Unit (GPU) for AI
A graphics processing unit (GPU) is a processor with many parallel cores, originally designed for rendering graphics, that has become the dominant hardware for training and running deep learning models because it performs matrix operations very efficiently.
Neural network training and inference consist largely of matrix multiplications, which GPUs execute in parallel across many cores. Data-centre GPUs add specialised units, such as NVIDIA's Tensor Cores, for low-precision matrix maths, and high-bandwidth memory to feed them. Software stacks such as NVIDIA CUDA and AMD ROCm, with libraries used by frameworks such as PyTorch and TensorFlow, make GPUs programmable for AI.
GPUs are used in clusters to train large models, in servers to host inference for LLMs and vision models, in workstations for development, and in embedded modules for edge AI in robots and inspection systems. Cloud providers offer GPU instances for on-demand capacity.
GPU memory capacity often determines which models fit, and memory bandwidth limits LLM token generation speed. Power consumption, cooling, availability and cost are significant planning factors. For many edge inference tasks, CPUs, integrated GPUs or NPUs can be more cost-effective than discrete GPUs.
Key points
- A massively parallel processor, dominant for deep learning
- Tensor units accelerate low-precision matrix maths
- CUDA and ROCm software stacks support AI frameworks
- Memory capacity and bandwidth often limit model size and speed
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.