Topical guide
GPU infrastructure for enterprise AI workloads
Training, fine-tuning, and inference infrastructure for large language models and other GPU-accelerated AI workloads. What to run where -- and how to keep the cost from becoming the project.
GPU use cases
What enterprise workloads need GPU compute
Not every AI workload needs GPUs. The ones that do have specific infrastructure requirements that differ significantly from general-purpose cloud compute.
LLM training and fine-tuning
Training large language models or fine-tuning foundation models on proprietary data requires GPU clusters with high-bandwidth interconnects (NVLink, InfiniBand) and distributed training frameworks (PyTorch DDP, DeepSpeed, Megatron). The infrastructure design determines whether training completes in hours or days.
Inference serving
Running LLMs or other neural networks in production requires low-latency GPU serving (vLLM, TensorRT-LLM, or NVIDIA Triton) with autoscaling, batching, and caching to keep cost-per-inference manageable at scale.
Computer vision and image generation
Object detection, image segmentation, and generative image models require GPU compute for both batch processing and real-time inference. The architecture differs significantly depending on whether latency or throughput is the primary constraint.
Scientific and HPC workloads
Molecular dynamics, climate simulation, genomics, and reservoir modelling all require GPU-accelerated compute. These workloads often combine cloud GPU instances with specialized on-premise hardware.
Infrastructure options
Cloud GPU vs. dedicated hardware
Each option has real trade-offs. The right choice depends on workload characteristics, data residency requirements, and the stability of your GPU demand.
AWS GPU instances
p3 (V100), p4 (A100), p5 (H100), g5 (A10G)
Best for
Flexible, pay-as-you-go training and inference. Spot instances for cost-effective training jobs.
Trade-offs
On-demand GPU pricing is expensive without reservations. Multi-node training requires careful networking configuration.
Azure GPU instances
NC series (V100, A100), ND series (A100, H100)
Best for
Microsoft ecosystem integration. Azure Machine Learning for MLOps. Good choice for organizations already on Azure.
Trade-offs
Availability can be constrained for newer GPU generations. Some regions have limited GPU SKU availability.
Google Cloud GPU
A2 (A100), A3 (H100), T4 for inference
Best for
TPU availability for Google-framework workloads. Good pricing for sustained use. Strong Kubernetes GPU support.
Trade-offs
TPU advantage is primarily for TensorFlow/JAX workloads. PyTorch workloads may be better on AWS or Azure.
Dedicated GPU hardware
NVIDIA DGX systems, custom GPU servers
Best for
Predictable cost for sustained heavy workloads. Data sovereignty -- compute stays entirely on-premise.
Trade-offs
High upfront capital cost. Limited scalability compared to cloud. Requires data centre capacity and GPU expertise to operate.
How we help
GPU infrastructure design and management
Common questions
GPU infrastructure -- FAQs
Does my enterprise AI project need GPUs?
It depends on the workload. Large language model training and inference almost always require GPUs. Smaller models can often run on CPU for inference. The practical test is whether your model runs at acceptable latency and throughput on CPU at your expected request volume.
Should we use cloud GPUs or buy dedicated hardware?
Cloud GPU instances make sense for most enterprises: no upfront cost, flexible capacity, and access to the latest hardware. Dedicated hardware makes sense when GPU utilization is consistently high, the workload runs 24/7, and data sovereignty requires that compute stays entirely on-premise.
How do we manage GPU infrastructure costs?
GPU cost optimization involves: spot instances for training jobs (60-70% cost reduction), reservations for predictable inference, inference optimizations (quantization, batching, caching), and autoscaling inference capacity. We typically reduce GPU spend 30-50% from initial deployment within 90 days.
Can we run GPU workloads in Canadian data centres?
Yes. AWS Canada, Azure Canada Central and East, and Google Cloud Montreal all offer GPU instances in Canadian regions, though availability of specific GPU SKUs varies. We design architectures that run all GPU compute in Canadian regions for organizations with data residency requirements.
Building GPU infrastructure for AI?
Tell us what you are trying to train or serve, at what scale, and with what data residency requirements. We will design the infrastructure that makes it work.