What Makes Up an AI Cloud Infrastructure Stack?
AI cloud infrastructure is more than a GPU instance with SSH access. Production AI workloads need five layers working together: compute, storage, networking, orchestration, and monitoring. Teams that treat GPU rental as the whole story discover the missing pieces when their first training job hangs on a network bottleneck or their inference endpoint times out under load.
The complete stack for AI workloads in the cloud:
- Compute layer: GPU instances (RTX 3090, A100, H100) with sufficient VRAM for your model size and batch dimensions
- Storage layer: High-throughput volumes for datasets, checkpoints, and model artifacts, with low-latency access from GPU instances
- Networking layer: Inter-GPU communication bandwidth (NVLink, InfiniBand, or high-speed Ethernet) for multi-GPU training
- Orchestration layer: Container runtimes, job schedulers, and workflow managers that turn GPU instances into a usable platform
- Monitoring layer: GPU utilization tracking, temperature monitoring, job logging, and cost attribution
BHK Cloud provides all five layers as a single integrated platform. GPU instances attach directly to storage volumes, container orchestration is built in, and monitoring dashboards are available from the moment you deploy.
How Does GPU Selection Affect Infrastructure Design?
The GPU you choose determines the rest of your infrastructure requirements. An RTX 3090 with 24 GB VRAM fits a 7B-parameter model in FP16. An 8-GPU node with A100 80 GB cards can train a 70B model with model parallelism. The infrastructure around the GPU must match the GPU's capabilities.
| GPU | VRAM | Best For | Storage Throughput Needed | Network Bandwidth Needed |
|---|---|---|---|---|
| RTX 3090 | 24 GB | Fine-tuning 7B models, inference, prototyping | 1-2 GB/s | 1 Gbps (single GPU) |
| A100 (40 GB) | 40 GB | Training 13B models, multi-GPU fine-tuning | 5-10 GB/s | 100-200 Gbps (NVLink) |
| A100 (80 GB) | 80 GB | Training 70B models, large-batch inference | 10-20 GB/s | 600 GB/s (NVLink) |
| H100 | 80 GB | Training 175B+ models, FP8 inference | 20-40 GB/s | 900 GB/s (NVLink 4) |
Most AI teams do not need H100 GPUs. For fine-tuning, inference, and small-to-medium training, the RTX 3090 provides the best price-performance ratio. BHK Cloud's $0.15/hr pricing makes it practical to run continuous experimentation without budget anxiety.
Why Does Storage Architecture Matter for AI Infrastructure?
Storage is the most overlooked component of AI infrastructure. A slow storage layer starves GPUs of data, and GPUs are expensive idle assets. At $0.15/hr, an idle GPU is a minor annoyance. At $3.00/hr, a data-starved GPU is a $72/day problem.
AI workloads have distinct storage access patterns:
- Training: Sequential read of large dataset files, periodic checkpoint writes. High throughput, moderate IOPS.
- Inference: Random read of model weights at startup, then mostly compute-bound. Low storage load after initialization.
- Data preprocessing: Random read/write of many small files. High IOPS, moderate throughput.
BHK Cloud's storage architecture places volumes on the same physical infrastructure as GPU instances. This eliminates network latency between compute and storage, and the $2.49/TB pricing with zero egress fees means you can keep datasets mounted permanently without worrying about access costs.
How to Build a Cost-Effective AI Cloud Stack
Start with the workload, not the infrastructure. The most common mistake in AI cloud infrastructure is overprovisioning: buying H100s for a workload that runs fine on RTX 3090s, or provisioning 100 Gbps networking for single-GPU training jobs.
- Profile your model. Measure VRAM usage, compute utilization, and data throughput on a single GPU before scaling to multiple.
- Right-size your GPU. If your model fits in 24 GB VRAM, an RTX 3090 at $0.15/hr is more cost-effective than an A100 at $1.10/hr. The performance difference is not 7x.
- Colocate storage and compute. Cross-region or cross-provider data access adds latency and egress costs. Keep datasets and checkpoints on the same infrastructure as your GPU instances.
- Use containerization. Docker images with pre-built dependencies eliminate environment setup time. BHK Cloud supports Docker-based deployments with persistent storage volumes.
- Monitor and iterate. Track GPU utilization. If you are consistently below 80%, your infrastructure is oversized or your data pipeline is the bottleneck.
Frequently Asked Questions
What is the minimum infrastructure needed to start training models?
A single RTX 3090 GPU with 24 GB VRAM, 50 GB of storage for datasets, and a Docker container with PyTorch or TensorFlow pre-installed. This setup costs approximately $0.15/hr on BHK Cloud and can fine-tune a 7B-parameter model in a few hours.
Does BHK Cloud support multi-GPU training?
Yes. BHK Cloud supports multi-GPU configurations with high-speed interconnects. For distributed training with frameworks like DeepSpeed or FSDP, multiple GPU instances can be provisioned in the same network zone.
How do I move my existing AI infrastructure to BHK Cloud?
Export your Docker images, upload your datasets via S3-compatible tools, and update your training scripts to point to BHK Cloud storage volumes. The migration is typically a one-day effort for a single-GPU setup.
What monitoring and observability tools are available?
BHK Cloud provides GPU utilization dashboards, storage throughput metrics, and job logging. You can also integrate your own monitoring stack (Prometheus, Grafana) with the GPU instances.