AI Colocation for Deep Learning-Compute & Storage Best Practices

AI and deep learning are no longer just tech industry buzzwords—they're embedded in how businesses operate, innovate, and compete. But here’s the thing: running deep learning workloads requires serious horsepower. We’re talking massive datasets, intense GPU compute needs, and tons of bandwidth. That’s where colocation for AI comes into play.

According to IDC, by 2025, over 50% of enterprise infrastructure will be deployed at colocation, hosting, or edge facilities rather than traditional data centers. It’s not just about outsourcing hardware anymore. It's about finding the right balance between compute performance, data locality, cost efficiency, and operational control.

If you're exploring AI colocation for deep learning workloads, this knowledge base will walk you through best practices for compute and storage—what to prioritize, how to optimize, and how cloud integration (especially with platforms like Cyfuture Cloud) plays a vital role in scaling smoothly.

Why AI Workloads Need Special Consideration

Let’s start with the basics. Deep learning models, especially LLMs or computer vision tasks, require enormous computational resources. A single training run can consume hundreds of GPU hours, produce terabytes of model checkpoints, and rely on fast access to high-throughput storage.

Trying to run this on a public cloud instance with default settings? Good luck with that performance bill. On-prem? It takes ages to scale.

Colocation strikes a middle path:

You colocate your high-performance gear in a data center

You get enterprise-grade connectivity and cooling

You maintain full control over your stack

You can still integrate with cloud (like Cyfuture Cloud) for storage, backups, or orchestration

Key Compute Considerations for AI Colocation

GPU Density and Rack Power

Deep learning isn’t CPU territory anymore. You need multiple NVIDIA A100s, H100s, or AMD Instinct GPUs.

A standard 42U rack might not cut it for power or heat.

Look for colocation providers that support high-density deployments (30kW+ per rack), liquid cooling options, or immersion cooling support.

Low-Latency, High-Bandwidth Networking

AI clusters work best with fast internal interconnects like InfiniBand or 100GbE+ Ethernet.

Ensure the colocation site allows custom networking setups. Some hosting providers restrict this.

Cyfuture Cloud-connected colo sites offer direct cloud peering to reduce egress latency when syncing with object storage or distributed training nodes in the cloud.

Redundant Power and Cooling Infrastructure

AI workloads can’t afford unplanned downtime. Your model checkpoint mid-training is not something you want to lose.

Always verify N+1 redundancy for power and HVAC.

Ask about uptime SLAs and battery backup duration.

Remote Management & KVM Access

You don’t want to drive down to the data center because a node froze.

Ensure the colo provider offers secure IP KVM or remote console access.

Out-of-band management tools should be part of the agreement.

Storage Strategies for Deep Learning Colocation

Now let’s talk storage. This is often underestimated until someone tries to load a 3TB dataset over a slow NFS mount. Here's what works:

Fast Local NVMe for Active Training

Place your training data and active models on ultra-fast local NVMe SSDs (PCIe 4.0 or 5.0 ideally).

This removes the storage bottleneck when your GPUs are crunching millions of parameters per second.

Tiered Storage Architecture

Use a hot-warm-cold model:

Hot: NVMe for active training

Warm: RAIDed SSDs for recent runs and evaluation

Cold: Object storage or HDDs for archival datasets, logs, etc.

Integrate with Cyfuture Cloud for seamless offsite object storage, backups, and snapshot recovery.

High IOPS File Systems

Training frameworks like TensorFlow and PyTorch prefer POSIX-compliant file systems with high throughput.

Consider BeeGFS, Lustre, or GPFS if your cluster is large and multi-node.

Avoid traditional network file systems unless tuned heavily for AI I/O patterns.

Data Sync and Replication

Regularly replicate checkpoints and model artifacts to the cloud (Cyfuture Cloud or similar) for resilience.

This enables hybrid setups where training happens on-prem, but inference or deployment can occur in the cloud.

Network & Integration Considerations

Cloud Peering and Hybrid Setups

Choose colocation partners that offer direct links to cloud providers, especially your preferred cloud (Cyfuture Cloud).

This hybrid approach enables:

Cheap bulk data storage in the cloud

Serverless orchestration using cloud APIs

Cloud-hosted dashboards and monitoring for on-prem AI workloads

Private Interconnects vs Public Internet

Don’t rely on the public internet for syncing large datasets.

Use direct cross-connects or private VLANs.

This minimizes latency and avoids bandwidth caps or security issues.

Security and Compliance

Deep learning data often includes sensitive PII or proprietary corp data.

Ensure the colocation facility is compliant with SOC 2, ISO 27001, and other relevant standards.

Use encrypted tunnels or VPNs when connecting to cloud-hosted services.

Operational & Cost Optimization Best Practices

Power Monitoring & Forecasting

AI workloads spike in power usage.

Monitor power draw per node and forecast demand for upcoming training cycles.

Cyfuture Cloud offers smart power analytics if using integrated services.

Containerization & Orchestration

Use Kubernetes (with GPU support) or Slurm for efficient resource scheduling.

This ensures that your expensive GPUs don’t sit idle.

Container-based training environments also simplify scaling across on-prem and cloud.

Colo Site Location Strategy

Choose a data center near your data sources or key users.

Cyfuture Cloud has strategic locations across India and global regions to minimize latency and data transfer costs.

Why Cyfuture Cloud Fits Well With AI Colocation

Cyfuture Cloud isn’t just another hosting platform—it's designed with enterprise AI scalability in mind:

Native integration with object storage for checkpointing and model backup

High-performance VMs for burst inference workloads

Interoperability with Kubernetes, Hadoop, and other orchestration frameworks

24/7 support that understands AI infrastructure challenges

For businesses running AI workloads that can't go all-in on public cloud or want more control over computers, combining Cyfuture Cloud services with colocation is a sweet spot.

Conclusion

Colocating your AI infrastructure isn’t just a smart cost move—it’s a performance strategy. But to get it right, you need to be intentional with your compute choices, storage architecture, and how everything connects to the cloud.

With the right setup—high-density GPUs, NVMe tiered storage, fast peering with Cyfuture Cloud, and smart orchestration tools—you get the best of both worlds: cloud agility and bare-metal power. That’s how modern enterprises scale deep learning without breaking the bank or the workflow.

So before you rack your next set of GPUs, make sure your AI colocation plan is built for performance, resilience, and growth. The future of machine learning depends on it.