Colocation

Colocation Infrastructure – Reimagined for AI and High-Performance Computing

The architecture of colocation is undergoing a paradigm shift, driven not by traditional enterprise IT, but by the exponential rise in GPU-intensive workloads powering generative AI, large language models (LLMs), and distributed training pipelines. Colocation today must do more than house servers it must thermodynamically stabilize multi-rack GPU clusters, deliver deterministic latency for distributed compute fabrics, and maintain power integrity under extreme electrical densities.

Why GPUs Break Legacy Colocation Infrastructure

Today’s AI systems aren’t just compute-heavy, they’re infrastructure-volatile. The training of a multi-billion parameter LLM on an NVIDIA H100 GPU cluster involves sustained tensor core workloads pushing 700+ watts per GPU, with entire racks drawing upwards of 40–60 kW under load. Even inference at scale, particularly with memory-bound models like RAG pipelines or multi-tenant vector search, introduces high duty-cycle thermal patterns that legacy colocation facilities cannot absorb.

Traditional colocation was designed for horizontal CPU scale, think 2U servers at 4–8 kW per rack, cooled via raised floor air handling. These facilities buckle under the demands of modern AI stacks:

i. Power densities exceeding 2.5–3x their design envelope.

ii. Localized thermal hotspots exceeding 40–50°C air exit temperatures.

iii. Inability to sustain coherent RDMA/InfiniBand fabrics across zones.

As a result, deploying modern AI stacks in a legacy colocation isn’t just inefficient it’s structurally unstable.

Architecting for AI: The Principles of Purpose-Built Colocation

Yotta’s AI-grade colocation data center is engineered ground-up with first-principle design addressing the compute, thermal, electrical, and network challenges introduced by accelerated computing. Here’s how:

1. Power Density Scaling: 100+ kW Per Rack: Each pod is provisioned for densities up to 100 kW per rack, supported by redundant 11–33kV medium voltage feeds, modular power distribution units (PDUs), and multi-path UPS topologies. AI clusters experience both sustained draw and burst-mode load spikes, particularly during checkpointing, optimizer backprop, or concurrent GPU sweeps. Our electrical systems buffer these patterns through smart PDUs with per-outlet telemetry and zero switchover failover.

We implement high-conductance busways and isolated feed redundancy (N+N or 2N) to deliver deterministic power with zero derating, allowing for dense deployments without underpopulating racks, a common hack in legacy setups.

2. Liquid-Ready Thermal Zones: To host modern GPU servers like NVIDIA’s HGX H100 8-GPU platform, direct liquid cooling isn’t optional it’s mandatory. We support:

– Direct liquid cooling

– Rear Door Heat Exchangers (RDHx) for hybrid deployments

– Liquid Immersion cooling bays for specialized ASIC/FPGA farms

Our data halls are divided into thermal density zones, with cooling capacity engineered in watts-per-rack-unit (W/RU), ensuring high-efficiency heat extraction across dense racks running at 90–100% GPU utilization.

3. AI Fabric Networking at Rack-Scale and Pod-Scale: High-throughput AI workloads demand topologically aware networking. Yotta’s AI colocation zones support:

– InfiniBand HDR/NDR up to 400 Gbps for RDMA clustering which allows data transfer directly between the memory of different nodes

– NVLink/NVSwitch intra-node interconnects

– RoCEv2/Ethernet 100/200/400 Gbps fabrics with low oversubscription ratios (<3:1)

Each pod is a non-blocking leaf-spine zone, designed for horizontal expansion with ultra-low latency (<5 µs) across ToRs. We also support flat L2 network overlays, container-native IPAM integrations (K8s/CNI plugins), and distributed storage backplanes like Ceph, Lustre, or BeeGFS critical for high IOPS at GPU memory bandwidth parity.

Inference-Optimized, Cluster-Ready, Sovereign by Design

The AI compute edge isn’t just where you train it’s where you infer. As enterprises scale out retrieval-augmented generation, multi-agent LLM inference, and high-frequency AI workloads, the infrastructure must support:

i. Fractionalized GPU tenancy (MIG/MPS)

ii. Node affinity and GPU pinning across colocation pods

iii. Model-parallel inference with latency thresholds under 100ms

Yotta’s high-density colocation is built to support inference-as-a-service (IaaS) deployments that span GPU clusters across edge, core, and near-cloud regions all with full tenancy isolation, QoS enforcement, and AI service-mesh integration.

Yotta also provides compliance-grade isolation (ISO 27001, PCI-DSS, MeitY-ready) with zero data egress outside sovereign boundaries enabling inference workloads for BFSI, health, and government sectors where AI cannot cross borders.

Hybrid-Native Infrastructure with Cloud-Adjacent Expandability

AI teams don’t want just bare metal they demand orchestration. Yotta’s colocation integrates natively with Shakti Cloud, providing:

i. GPU leasing for burst loads

ii. Bare-metal K8s clusters for CI/CD training pipelines

iii. Storage attach on demand (RDMA/NVMe-oF)

This hybrid model supports training on-prem, bursting to cloud, inferring at edge all with consistent latency, cost visibility, and GPU performance telemetry. Whether it’s LLM checkpoint resumption or rolling out AI agents across CX platforms, our hybrid infra lets you build, train, and deploy without rebuilding your stack.

Conclusion

In an era where GPUs are the engines of progress and data is the new oil, colocation must evolve from passive hosting to active enablement of innovation. At Yotta, we don’t just provide the legacy colocation, we deliver AI-optimized colocation infrastructure engineered to scale, perform, and adapt to the most demanding compute workloads of our time. Whether you’re building the next generative AI model, deploying inference engines across the edge, or running complex simulations in engineering and genomics, Yotta provides a foundation designed for what’s next. The era of GPU-native infrastructure has arrived and it lives at Yotta.