Workloads
AI workloads fall into four primary categories — training, inference, fine-tuning, and edge AI — each with distinct compute, memory, latency, and networking requirements that dictate infrastructure design.
Training is the most compute-intensive AI workload, requiring weeks to months of continuous GPU operation on large datasets. Frontier model training (e.g., GPT-4-class) requires thousands of GPUs operating in parallel with high-bandwidth interconnects. Training workloads demand maximum GPU utilisation, non-blocking network fabric, and high-throughput storage for checkpoint management.
Infrastructure requirements: high GPU density (8–16 GPUs per node), InfiniBand or high-bandwidth GPU interconnect interconnects, 80+ kW rack power, liquid cooling, and parallel file systems capable of 100+ GB/s throughput.
Inference is the production deployment of trained AI models — processing user queries and generating responses. Inference workloads are latency-sensitive and bursty, requiring auto-scaling GPU capacity and optimised serving infrastructure. While individual inference calls use less compute than training, aggregate inference demand typically exceeds training demand once models reach production scale.
Infrastructure requirements: moderate GPU density (1–8 GPUs per node), standard networking for API serving, 10–30 kW rack power, air or liquid cooling, and high-availability (99.95%+) SLAs with geographic redundancy.
Fine-tuning adapts pre-trained foundation models to specific enterprise use cases using smaller, domain-specific datasets. Retrieval-Augmented Generation (RAG) combines inference with real-time data retrieval. Both workloads require moderate GPU capacity (1–8 GPUs), flexible scheduling, and secure data isolation — making them ideal for multi-tenant GPU cloud environments.
Infrastructure requirements: moderate GPU density, flexible scheduling, secure multi-tenancy, and integration with enterprise data sources and vector databases.
Edge AI deploys inference models closer to data sources — on devices, gateways, or micro-data centers — to reduce latency, preserve bandwidth, and maintain data sovereignty. Edge AI infrastructure includes compact GPU appliances, edge servers, and hybrid cloud-edge orchestration platforms.
Infrastructure requirements: low-power GPU appliances (100–500W), ruggedised enclosures, intermittent connectivity support, and centralised model management and OTA updates.
Training is the process of building an AI model by processing large datasets on GPU clusters over weeks or months. Inference is using a trained model to generate responses in production. Training requires far more compute and infrastructure investment, while inference requires low latency and high availability.
Fine-tuning takes a pre-trained foundation model and adapts it to a specific domain or use case using a smaller, specialised dataset. It requires significantly less compute than full training (typically 1–8 GPUs for hours rather than thousands for weeks) and is ideal for enterprise-specific AI applications.
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