GPU Computing
The use of graphics processing units for general-purpose parallel computation, providing the massive throughput needed for training and running AI models.
In Depth
GPU computing, also known as GPGPU (General-Purpose computing on Graphics Processing Units), leverages the massively parallel architecture of graphics processors for computational workloads beyond traditional graphics rendering. Modern GPUs contain thousands of cores optimized for parallel floating-point operations, making them ideally suited for the matrix multiplications and tensor operations that dominate AI training and inference workloads.
NVIDIA dominates the AI GPU market with its data center GPU lineup. The H100 and H200 GPUs based on the Hopper architecture deliver up to 3,958 teraflops of FP8 performance and feature the Transformer Engine, a hardware unit specifically designed to accelerate transformer-based model computations. The newer Blackwell architecture (B100, B200, GB200) further increases performance with second-generation Transformer Engines and higher memory bandwidth. For inference workloads, the L40S and A10G provide cost-effective options, while the Grace Hopper Superchip combines CPU and GPU in a unified architecture.
GPU infrastructure for AI takes several forms: cloud GPU instances from providers like AWS, Azure, GCP, and Oracle; NVIDIA DGX systems that package multiple GPUs with optimized networking and software into turnkey AI appliances; custom-built GPU clusters with high-speed interconnects like NVLink and InfiniBand; and edge GPU platforms like NVIDIA Jetson for deployment at remote locations. Multi-GPU and multi-node training requires specialized software frameworks like DeepSpeed, Megatron-LM, and FSDP that handle distributed computation, communication, and memory management.
The economics of GPU computing are a primary driver of AI infrastructure decisions. GPU costs dominate AI operational budgets, making efficient utilization critical. Techniques like mixed-precision training, gradient checkpointing, model parallelism, and workload scheduling maximize the return on GPU investment. Understanding GPU capabilities, memory hierarchies, and interconnect topologies is essential for designing AI infrastructure that delivers required performance within budget constraints.
Related Terms
CUDA
NVIDIA proprietary parallel computing platform and API that enables developers to use NVIDIA GPUs for general-purpose processing and AI workloads.
TensorRT
NVIDIA high-performance deep learning inference optimizer and runtime that maximizes throughput and minimizes latency on NVIDIA GPUs.
Inference
The process of running a trained AI model to generate predictions or outputs from new input data, as opposed to the training phase.
Edge Inference
Running AI model inference directly on local devices or edge hardware near the data source, rather than sending data to cloud servers for processing.
Latency Optimization
Techniques and engineering practices that reduce the response time of AI systems from input to output for better user experience and throughput.
Related Services
Private & Sovereign AI Platforms
Designing air-gapped and regulator-aligned AI estates that keep sensitive knowledge in your control. NVIDIA DGX, OCI, and custom GPU clusters with secure ingestion, tenancy isolation, and governed retrieval.
Edge & Bare Metal Deployments
Planning and operating GPU fleets across factories, research hubs, and remote sites. Jetson, Fleet Command, and bare metal roll-outs with zero-trust networking and remote lifecycle management.
Cloud AI Modernisation
Refactoring AWS, Azure, GCP, and Oracle workloads into production-grade AI stacks. Multi-cloud RAG pipelines, observability, guardrails, and MLOps that slot into existing engineering rhythms.
Related Technologies
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