Extreme Power Density
Three-phase power from 10kW all the way to 50kW+ per rack. You get the density modern GPU clusters need without leaving capacity unused.
PowerVyomCloud ML Colocation
High-Density ML Colocation
for GPU Clusters
- 10–50kW+ GPU Rack Capacity
- Direct-to-Chip & Rear-Door Cooling
- InfiniBand + 400G Ethernet Ready
- BGP, ASN & Private Network Control
- Cloud Cross-Connects for Hybrid AI
- Reserve Adjacent Capacity for Cluster Growth
ISO 27001 certified · N+1 power and cooling · 99.999% power SLA on HPC Rack plans
Don't own GPUs yet?
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Rent GPU servers instead - we own the hardware
₹1,00,000
ML Colocation Starts From / Month
10–50kW+
Power Per Rack
99.999%
Power SLA (HPC Rack)
ISO 27001
Certified Infrastructure
Verify it yourself
Test our network on the Looking Glass
Run live ping and traceroute checks from our data centers to see the latency and routing for yourself — no signup needed.
Open the Looking GlassChoose your best Server Colocation plan
- 24/7 NOC Support
- Remote Hands Included
- DDoS Protection Included
| Plan | Rack Space | Power | Network | Bandwidth | Monthly Price (₹) | Order Now |
|---|---|---|---|---|---|---|
| 1U Server Colocation | 1U | 0.5 kW Power Included | 1 Gbps Network Port | 10 TB Bandwidth | ₹5,251/mo Renews at the same price | Get Now |
| Quarter Rack Colocation | 10U | 1.5 kW Power Included | 1 Gbps Network Port | 30 TB Bandwidth | ₹17,711/mo Renews at the same price | Get Now |
| Half Rack Colocation | 21U | 3 kW Power Included | 10 Gbps Network Port | 50 TB Bandwidth | ₹31,061/mo Renews at the same price | Get Now |
| Full Rack Colocation | 42U | 6 kW Power Included | 10 Gbps Network Port | 100 TB Bandwidth | ₹57,761/mo Renews at the same price | Get Now |
All plans include remote hands, DDoS protection, 24/7 NOC monitoring and a 99.5% uptime SLA. Need a custom cage? Talk to sales.
100% Risk-Free with a 7-Day Money-Back Guarantee
We are confident in the quality of our services. If you're not satisfied, we offer a full refund within 7 days.
VyomCloud ML Colocation vs Cyfuture and Cloud GPU Instances
| You Own the Hardware | |||
| Factor |
VyomCloud ML Colocation |
Cyfuture AI Colocation |
Hyperscale Cloud GPU |
|---|---|---|---|
| Model | You bring the hardware. We handle the rack, power, cooling and network. | AI-focused colocation with high-density power and liquid cooling. | You rent the provider's GPUs by the hour or on commitment. |
| Rack density | 10kW, 20–30kW and 100kW+ options | 20kW to 240kW+ depending on cooling and hardware | Not applicable — capacity is fixed by the instance you pick |
| Cooling | Direct-to-chip, rear-door heat exchangers and liquid cooling support | Direct-to-chip, RDHx, hybrid air/liquid and CDU options | Fully managed by the provider |
| HPC networking | InfiniBand and 400G Ethernet with leaf-spine architecture | Not clearly listed | Provider-defined, varies by instance |
| Network control | BGP, cloud cross-connects and dark-fiber options | High-density AI setup — confirm BGP/dark-fiber details | Mostly controlled by the provider |
| Pricing visibility | Clear published tiers: ₹1,00,000 / ₹2,50,000 / ₹10,00,000 per month | Quote-based | Usage-based (hourly, reserved, etc.) |
| Certifications | ISO 27001 | – | – |
| Hardware owner | You own the hardware | You own the hardware | Provider owns the GPUs |
Still not sure which setup fits? Contact our team or use our Server Finder to receive a personalised recommendation in seconds.
Power · Cooling · Network · Support
Features of ML Colocation (VyomCloud)
On-Site HPC Engineering
HPC engineers, remote hands and managed hands help with complex cabling, optics and power work.
SupportPrecision Cooling & N+1
Tightly controlled environments plus N+1 power and cooling keep long training runs stable.
CoolingHPC Networking
InfiniBand and 400G Ethernet support fast node-to-node traffic, which matters when you're trying to keep multi-GPU utilisation high.
NetworkLow-Latency Cloud Cross-Connects
Direct fiber links to major clouds give you private, predictable paths for hybrid training and inference.
NetworkDedicated Fiber Options
Dark fiber lets you run high-capacity private links for secure dataset and checkpoint replication between sites.
NetworkEnhanced Asset Protection
Extra physical security measures protect high-value GPU and TPU hardware.
SecurityElastic Rack Capacity
You can reserve adjacent racks so your cluster can grow without being split across rooms.
CapacityReal-World Use Cases of ML Colocation
Large-Model Training Clusters
Teams running long training jobs can use high-density, liquid-cooling-ready racks with InfiniBand or 400G networking.
Private AI Superpods
Labs that need dedicated 100kW+ clusters get liquid cooling, custom fire protection and private operations support.
HPC and CPU-Heavy Rendering
Research and rendering groups can run dense 10kW racks with A+B power, cold aisle containment and smart PDUs.
Research Labs & POC Builds
Universities and startups can test hardware with flexible leases, test benches and optional pre-configured environments.
Moving Beyond Cloud GPU Costs
If your GPUs run most of the time, colocating owned hardware often works out cheaper while still giving you private cloud links for bursts.
Private AI Inference
Companies that need to keep inference on private hardware can still use hybrid connectivity when traffic spikes.
Multi-Site AI Infrastructure
Organisations can link clusters across locations with high-capacity private connections for replication and data movement.
Your GPUs. Our Facility. Your Network.
Why Choose VyomCloud for ML Colocation?
Pick from rack, GPU, suite or lab options based on what your hardware actually needs.
Three clear tiers starting at ₹1,00,000 per month, so you can budget before talking to sales.
BGP, your own ASN, cloud cross-connects and dark fiber — you stay in control of the network. See our IP transit service for upstream capacity.
On-site engineers help with HPC cabling, optics, power distribution and GPU deployments.
Why Businesses Choose VyomCloud for ML Colocation
01 · AI-First Startups
AI-First Startups
Move steady training workloads off rented GPUs onto owned hardware for more predictable monthly costs.
- Fixed monthly cost
- Easier runway planning
01
02 · Data-Sensitive Enterprises
Data-Sensitive Enterprises
Keep training data on hardware you own, inside a controlled facility, with tighter control over what leaves the environment.
- Control your training data
- Manage network access
02
03 · Research & University Groups
Research & University Groups
Use short-term colocation and a test bench to validate hardware before locking in research or grant money.
- Test hardware first
- Reduce long commitments
03
04 · Engineering & Infrastructure Teams
Engineering & Infrastructure Teams
Reserve adjacent capacity early so future expansion doesn't force major re-cabling or redesign.
- Plan cluster expansion
- Avoid major redesigns
04
05 · Hybrid AI Infrastructure
Hybrid AI Infrastructure
Combine owned training hardware with private cloud cross-connects when inference demand temporarily spikes.
- Train on owned hardware
- Burst to cloud when needed
05
06 · Growing AI Infrastructure
Growing AI Infrastructure
Add physical capacity as workloads grow, without having to redesign the whole setup.
- Add capacity as needed
- Support workload growth
06
07 · Future-Ready AI Facilities
Future-Ready AI Facilities
Infrastructure built for rising chip power draws helps teams plan longer-term deployments.
- Prepare for higher power
- Plan multi-year deployments
07
08 · Renewable Energy Options
Renewable Energy Options
Renewable energy choices can support sustainability goals when you're committing to long-term, power-hungry AI infrastructure.
- Support sustainability goals
- Consider long-term impact
08
What Our Clients Say
Hear from businesses that trust Vyom Cloud for their infrastructure needs
"Operating 8x H100 GPU nodes requires unmatched thermal stability. VyomCloud's high-density power and cold aisle containment delivered 100% uptime through continuous model training cycles."
"We transitioned our computer vision inference pipelines to a dedicated GPU Cluster rack. Sub-10ms network latency and published cross-connect rates saved us over 40% vs hyperscale cloud."
"Having 24/7 smart hands who understand high-power GPU hardware makes running our ML clusters hassle-free without flying engineers across states."
"The private AI suite setup with dedicated BGP control and 400G Ethernet capability gave us the exact data sovereignty and throughput we needed."
FAQ
ML Colocation FAQs
Everything you need to know before you bring your GPU cluster to VyomCloud.
It's a service where you put your own GPU, TPU or accelerator servers into a data centre built for high power and heat density. The provider supplies the space, power, cooling and network. You own and manage the hardware. The main difference from regular colocation is how much power and cooling each rack can actually deliver.
Yes. We support direct-to-chip cooling and rear-door heat exchangers. The AI Suite tier includes a liquid cooling loop. The GPU Cluster tier lists rear-door heat exchange and direct liquid support. The right method depends on your server vendor's requirements, so check with sales before ordering hardware.
If the GPUs run most of the time over a year or longer, owning the hardware in colocation is usually cheaper, though you take on the upfront cost and maintenance. For short or unpredictable workloads, cloud rental is simpler and often cheaper. Many teams do both: owned racks for steady load, cloud for bursts. If you'd rather not buy hardware, you can rent GPU servers instead.
We offer direct fiber cross-connects to major cloud providers and exchanges for hybrid workflows. Availability of specific providers and regions depends on the facility. For BGP and upstream capacity, see our IP transit service.
The ML Lab tier is built for this. It offers shared or private space, short-term leases, test bench access, hardware leasing and pre-configured environments for research, dev/test and inference.
We provide enhanced physical security for high-value GPU and TPU hardware, including 24/7 monitoring, biometric access control and CCTV. The infrastructure is ISO 27001 certified. Ask sales for the exact security controls at the facility where your racks will sit. For inference endpoints, you can add DDoS protection.
Most setups need 20kW to 40kW or more. A single modern 8-GPU server can draw around 10kW, so even a half-filled rack quickly outgrows a standard 5–10kW budget. VyomCloud supports roughly 10kW to 50kW+ per rack, with a private suite option at 100kW+. If your needs are lower, see our full rack colocation for standard-density racks.
Published pricing starts at ₹1,00,000 per month for an HPC Rack (10kW), ₹2,50,000 for a GPU Cluster rack (20–30kW), and ₹10,00,000 for an AI Suite (private suite, 100kW+). ML Lab is custom-priced. Cross-connects, bandwidth and hands-on services may be charged separately. You can also see all pricing.
With colocation you buy and own the servers and place them in our racks. With a GPU dedicated server, we own the hardware and you rent it. Colocation gives you control over the exact hardware and long-term cost. Renting gives you speed and no capital outlay. Explore our dedicated servers or custom bare metal setups if renting suits you better.
Yes. You can reserve adjacent capacity so growth stays in one place. For larger builds, the AI Suite tier gives you a private cage or suite with dedicated power and operations support. Smaller deployments can start with smaller colocation options such as the 1U rack.
HPC Rack includes remote hands. GPU Cluster includes managed hands. AI Suite includes 24/7 dedicated operations. Our standard colocation terms include 30 minutes of remote hands per month, with more available on demand.