What is AI Infrastructure?
AI infrastructure is the foundation that powers machine learning workloads — from training large language models to serving real-time predictions. It includes hardware (GPUs, storage, networking) and software (MLOps tools, orchestration platforms).
Why It Matters
The right infrastructure can reduce training time from weeks to days, cut costs by 70%, and enable experiments that were previously impossible. Bad infrastructure creates bottlenecks that slow down your entire AI team.
Core Components
Compute Layer
GPUs, TPUs, and CPUs for training and inference
- • NVIDIA A100/H100 GPUs
- • CPU clusters for preprocessing
- • Inference servers
Storage Layer
High-speed storage for datasets and models
- • Object storage (S3/GCS)
- • High-speed NVMe arrays
- • Distributed file systems
Networking
High-bandwidth connections for distributed training
- • InfiniBand/RoCE
- • 100GbE+ networking
- • GPU-to-GPU interconnects
MLOps Platform
Tools for managing the ML lifecycle
- • Experiment tracking
- • Model registry
- • CI/CD pipelines
Compute Layer
The compute layer is the heart of AI infrastructure. GPUs handle the heavy lifting of matrix operations that power neural networks.
| Option | Pros | Cons | Best For |
|---|---|---|---|
| On-Premise GPUs | Full control, predictable costs | High upfront cost, maintenance | Large enterprises |
| Cloud GPUs (AWS/GCP) | Scalable, no maintenance | Can be expensive at scale | Variable workloads |
| Griddly Cloud | 70% cheaper, instant access | Newer platform | Cost-conscious teams |
| Hybrid | Best of both worlds | Complex to manage | Enterprise AI teams |
Storage & Data
AI workloads are data-hungry. You need fast storage for training data, model checkpoints, and experiment artifacts. The storage layer often becomes a bottleneck if not properly designed.
Networking
For distributed training across multiple GPUs, networking is critical. InfiniBand and high-speed Ethernet enable efficient gradient synchronization.
Key Metric: GPU-to-GPU Bandwidth
For large model training, you need 400-800 Gbps between GPUs. NVLink provides 900 GB/s within a node, while InfiniBand handles inter-node communication at 400 Gbps.
MLOps & Orchestration
MLOps tools help you manage the entire ML lifecycle — from experiment tracking to model deployment and monitoring.
Experiment Tracking
Model Registry
Orchestration
Serving
Build vs Buy
Build Your Own
- Full control over hardware
- Predictable long-term costs
- Custom optimizations
Use Cloud/Griddly
- No upfront investment
- Instant scalability
- No maintenance burden