AI Training Workloads
AI training refers to the process of building and optimizing machine learning models — especially large language models (LLMs), vision transformers, multimodal systems, and foundation models. Unlike inference, which runs continuously at the edge, training is batch-oriented, massively parallel, and energy intensive. Training workloads are the primary driver behind AI factories and hyperscale GPU deployments, pushing data centers into the multi–100 MW class.
Overview
- Purpose: Transform raw datasets into optimized models via iterative forward and backward passes.
- Scale: State-of-the-art LLMs use tens of thousands of GPUs, 10–100+ MW power, multi-month training runs.
- Characteristics: High-bandwidth fabrics, checkpointing to parallel storage, mixed precision (FP8/16), gradient accumulation.
- Comparison: Training is throughput-driven (time-to-train), while inference is latency-driven (time-to-response).
Training Workflow
- Data ingestion: Curating, filtering, tokenizing terabytes–petabytes of raw data.
- Sharding & batching: Distributed loading of datasets across thousands of GPUs.
- Forward pass: Compute outputs given inputs and weights.
- Backward pass: Gradient calculation and backpropagation.
- Optimizer step: Update weights using SGD, AdamW, or newer optimizers.
- Checkpointing: Frequent model state saves to enable recovery and evaluation.
Bill of Materials (BOM)
| Domain | Examples | Role |
|---|---|---|
| Accelerators | NVIDIA H100/H200, AMD MI300X, TPU v5e/v5p | Parallel matrix/tensor compute for training |
| Compute Nodes | 8–16 GPU servers, NVLink/NVSwitch | Building blocks of training clusters |
| Fabric / Interconnect | NVIDIA InfiniBand NDR, NVLink, HPE Slingshot | All-to-all GPU communication at µs latencies |
| Storage | DDN, Weka, Lustre, BeeGFS | High-throughput parallel file systems for checkpoints/datasets |
| Schedulers | Kubernetes, Slurm, Ray | Orchestration of distributed jobs |
| Cooling | Direct-to-chip liquid, immersion | Manage >80 kW per rack thermal loads |
| Power | MV feeds, solid-state transformers, BESS | Provide steady 10–100 MW loads for training halls |
Facility Alignment
| Training Mode | Best-Fit Facilities | Notes |
|---|---|---|
| Frontier-scale training | AI Factories, HPC Supercomputers | Tens of thousands of accelerators, exaflop-class |
| Enterprise fine-tuning | Enterprise DCs, Colocation | Smaller GPU clusters (dozens–hundreds of GPUs) |
| Academic/research HPC | Supercomputers, university HPC clusters | Shared national resources, grant-funded |
| Cloud-based training | Hyperscalers | Elastic GPU instances (AWS P5, Azure NDv5, GCP TPU pods) |
Key Challenges
- Scale: Coordinating 10k–100k GPUs with high utilization.
- Energy: Multi–100 MW loads stress regional grids, require microgrids and DER integration.
- Thermals: Air cooling insufficient; liquid and immersion cooling required.
- Data Pipeline: Datasets must be cleaned, tokenized, and streamed at Tb/s scale.
- Fault Tolerance: Node/rack failures must not stall multi-month jobs; checkpointing critical.
- Cost: Frontier model training runs cost $50M–$100M+ in compute + energy.
- Supply Chain: GPU/ASIC shortages and long lead times constrain deployment.
Notable Training Clusters
| Cluster | Operator | Scale | Notes |
|---|---|---|---|
| NVIDIA Eos | NVIDIA | 4k+ H100 GPUs | DGX SuperPod architecture |
| Meta Research SuperCluster (RSC) | Meta | 16k+ GPUs | LLM + multimodal research |
| Tesla Colossus (planned) | Tesla/xAI | 100k+ GPUs | Training for FSD and Optimus humanoids |
| Aurora | Argonne National Lab | Exascale-class | HPC + AI hybrid workloads |
Future Outlook
- Exascale AI: Clusters moving toward 1–10 EF peak compute, powered by liquid cooling and 100 MW+ campuses.
- Convergence: HPC and AI training workloads blending — simulation + generative AI hybrids.
- Specialized Silicon: Growth in domain-specific chips (TPU, Groq, Tenstorrent) beyond GPUs.
- AI Model Training as a Service: Hyperscalers renting massive GPU pods via APIs.
- Sustainability: Increasing pressure for carbon-aware scheduling and renewable-only training runs.
FAQ
- How is training different from inference? Training is throughput- and energy-intensive; inference is latency-sensitive and runs continuously.
- What’s the largest training cluster today? Frontier HPC (US), Aurora (US), and Meta RSC are among the largest public clusters; Tesla Colossus is planned.
- Why does training require so much energy? Tens of thousands of GPUs run at full utilization for weeks or months; power draw is constant and massive.
- Can training run in the cloud? Yes — hyperscalers rent GPU clusters, but cost scales quickly compared to on-prem AI factories.
- What’s next after GPUs? Custom ASICs (TPUs, domain accelerators) and optical interconnects for scaling beyond GPU bottlenecks.