Full, Dedicated GPU Access
Every plan gives you complete, exclusive use of the GPU’s VRAM and compute cores. No slicing. No sharing with another customer’s training job.
GPUVyomCloud GPU Dedicated Servers
GPU Dedicated Servers, Real NVIDIA Hardware
with Full System Pricing
- 100% Dedicated NVIDIA GPU, No Sharing
- RTX 4090, A100 & Up to 4× H100
- Full-System Pricing, GPU + CPU + RAM + Storage
- NVLink/NVSwitch for Multi-GPU Training
- CUDA + cuDNN Pre-Installed on Pro & Enterprise
- Scale From 1 GPU to 64-GPU InfiniBand Clusters
7-day money-back guarantee · Full root access · CUDA-ready from day one
Need more than 4 GPUs?
View Bare Metal
See our custom bare metal
₹39,999
GPU Plans From, Per Month
4× H100
NVLink/NVSwitch Enterprise Tier
64-GPU
InfiniBand Cluster Option
7-Day
Money-Back Guarantee
Verify it yourself
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Open the Looking GlassChoose your best gpu plan
- 24/7 Expert Support
- Instant Setup
- Great fit for dev teams and admins
| Plan | vCPU Cores | RAM (GB) | Traffic (TB) | Bandwidth (Mbps) | Storage (GB) | Monthly Price (₹) | Get Now |
|---|---|---|---|---|---|---|---|
| GPC.NANO02 | 1 | 2 | 0.50 | 100 | 20 | ₹360/mo Renews at the same price | Get Now |
| GPC.MICRO01 | 2 | 2 | 1.00 | 100 | 40 | ₹560/mo Renews at the same price | Get Now |
| GPC.MICRO02 | 2 | 4 | 2.00 | 250 | 40 | ₹1,040/mo Renews at the same price | Get Now |
| GPC.SMALL | 4 | 8 | 5.00 | 250 | 80 | ₹1,600/mo Renews at the same price | Get Now |
| GPC.MEDIUM | 4 | 16 | 10.00 | 500 | 160 | ₹2,640/mo Renews at the same price | Get Now |
| GPC.LARGE | 8 | 16 | 10.00 | 500 | 160 | ₹3,200/mo Renews at the same price | Get Now |
| GPC.XLARGE01 | 8 | 32 | 30.00 | 500 | 320 | ₹6,800/mo Renews at the same price | Get Now |
| GPC.XLARGE02 | 16 | 64 | 60.00 | 1000 | 1024 | ₹10,000/mo Renews at the same price | Get Now |
| CPUO-MICRO | 4 | 4 | 2.00 | 250 | 40 | ₹1,200/mo Renews at the same price | Get Now |
| CPUO-SMALL | 8 | 8 | 5.00 | 500 | 80 | ₹1,760/mo Renews at the same price | Get Now |
| CPUO-MEDIUM | 16 | 32 | 10.00 | 500 | 160 | ₹7,360/mo Renews at the same price | Get Now |
All plans include full root access, 1 IPv4 + IPv6, DDoS protection and a 99.5% uptime SLA. Need something larger? 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.
How VyomCloud Compares
Compare GPU server capabilities, pricing, scalability and multi-GPU infrastructure across leading dedicated GPU server providers.
| Factor | VyomCloud | OVHcloud (Bare Metal) | ServerMania | HorizonIQ |
|---|---|---|---|---|
| Top GPU |
NVIDIA H100 (up to 4x with NVLink/NVSwitch)
|
NVIDIA L40S on higher tiers; L4 on lower tiers
|
RTX PRO Blackwell 6000; no H100/H200
|
NVIDIA H200
|
| Entry Price |
₹39,999/mo (RTX 4090)
|
Higher for comparable L4 configurations
|
Custom quote only
|
$500/mo for L40S (GPU only — full system priced separately)
|
| Pricing Style |
Full system price published for every tier
|
Published, but GST excluded
|
Fully custom
|
GPU-only price shown; rest requires a sales call
|
| Max GPUs per Server |
Up to 4 (Enterprise), 8–64 (Custom Cluster)
|
Not clearly stated
|
Custom
|
Capped at 2
|
| Multi-GPU Interconnect |
NVLink/NVSwitch + InfiniBand option
|
Limited information
|
Limited information
|
Limited by 2-GPU cap
|
| Best For |
Teams that need real H100-class training hardware
at a clear, full-system price
|
Inference and lighter rendering work
|
Fully custom builds
|
H100/H200 if you don't mind a sales conversation
|
Figures change. Always double-check current pricing and specs.
100% dedicated NVIDIA GPUs, no sharing
Features of GPU Dedicated Servers
A Real Range, From Starter to Multi-H100
GPU plans scale from RTX 4090 for inference to A100 for training, 4× H100 for large-scale AI/HPC, and 8–64 GPUs with InfiniBand for advanced enterprise workloads.
RangeCUDA, cuDNN, and TensorRT Pre-Installed
On Professional and above the core ML stack is ready at first boot. You don’t waste the first few hours just getting drivers and libraries working.
SoftwareHigh-Speed Interconnect on Multi-GPU Tiers
The 4× H100 Enterprise setup uses fast GPU-to-GPU links. That matters when models need to talk to each other constantly during training.
InterconnectFull Root Access and IPMI
You control the OS and software completely. Higher tiers also include IPMI for out-of-band management when SSH isn’t enough.
ControlNetwork Bandwidth Matched to the Tier
1 Gbps on Starter, 10 Gbps unmetered on Professional, 25 Gbps dedicated on Enterprise. Useful when you’re moving large datasets or checkpoints.
NetworkA Clear Path to Multi-Node Clusters
Custom GPU Cluster extends the same tier structure into real multi-node territory with InfiniBand, instead of forcing you onto a completely different product line.
ClustersDDR5 ECC Memory on Every Tier
Error-correcting memory lowers the chance of silent data corruption during long training runs.
MemoryReal-World Use Cases of GPU Dedicated Servers
Training and Fine-Tuning Large Language Models
A team fine-tuning an open-source model on its own data needs steady, high-VRAM access for days. GPU Professional’s A100 or Enterprise’s multi-H100 setup is built for exactly that.
AI Inference at Production Scale
A SaaS product serving AI features to real users needs consistent, low-latency inference without sharing the GPU with someone else’s batch job. The Starter tier’s RTX 4090 handles this well.
Computer Vision and Image Recognition
A logistics company running real-time package scanning needs dedicated throughput that doesn’t drop when other work is running. Shared or virtualized GPUs often struggle here.
3D Rendering and VFX Production
Studios rendering complex scenes need GPU time they can reserve for the full job without competing with other tenants.
Scientific Simulation and HPC Workloads
Research teams running physics or chemistry simulations benefit from the same parallel power used in AI training. The Custom Cluster’s InfiniBand interconnect is designed for this kind of multi-node work.
Startups Building an AI Product Without Cloud Queue Delays
Early-stage teams that keep hitting capacity limits or long queues on big cloud platforms get a dedicated GPU that’s always available.
Research Labs Scaling Past a Single Server
Labs whose work has outgrown one machine use the Custom Cluster’s multi-node setup to train across several servers as one system.
Data Analytics on Large Datasets
Teams doing GPU-accelerated data processing (not just AI training) get the same parallel advantage for big ETL or feature-engineering jobs that would crawl on CPU alone.
Real GPUs. Full-System Pricing.
Why Choose VyomCloud for GPU Dedicated Servers?
VyomCloud publishes a 4x H100 NVLink configuration for demanding AI training and inference workloads.
Published pricing includes the GPU, CPU, RAM, storage, and network, avoiding separate hardware cost surprises.
Custom GPU Cluster options scale from 8 to 64 GPUs with InfiniBand for larger AI workloads.
CUDA, cuDNN, and TensorRT come pre-installed on Professional and higher tiers for faster deployment.
Why Businesses Choose VyomCloud for GPU Hosting Needs
01 · Budget Certainty
A Full System Price, Not a GPU-Only Teaser
Published full-system pricing means finance can budget for AI infrastructure as a known monthly cost, rather than discovering the real price only after a sales call.
- Full system price published
- No post-quote cost surprises
01
02 · Training Capability
Access to Hardware That Actually Trains Large Models
A published 4x H100 NVLink tier means a business doesn't need an enterprise sales relationship just to get access to genuine large-model training hardware.
- H100-class hardware, published
- No negotiation required to access it
02
03 · Faster Iteration
Less Time Lost to Environment Setup
CUDA, cuDNN, and TensorRT pre-installed on higher tiers mean a data science team starts running experiments sooner after a server is provisioned.
- Pre-installed ML stack
- Faster time to first training run
03
04 · Capacity Certainty
No Competing for Cloud GPU Availability
A dedicated GPU server is available when you need it, without the capacity queues that can affect popular GPU instance types on major cloud platforms during high-demand periods.
- Dedicated, always-available GPU
- No cloud capacity queueing
04
05 · Growth Path
From a Single GPU to a Research Cluster
Moving from GPU Starter through Enterprise, or into a Custom GPU Cluster, means a growing AI team doesn't need to re-platform onto a different provider as its compute needs scale.
- Clear tier progression
- Multi-node clusters available
05
06 · Data Control
Full Root Access Over Where Your Models Live
Training proprietary models on infrastructure you fully control, rather than a shared cloud GPU environment, matters for teams with IP or data sensitivity concerns around their training data or model weights.
- Full root access
- No shared-tenant exposure
06
What Our Clients Say
Hear from businesses that trust Vyom Cloud for their infrastructure needs
"We needed a GPU for six weeks of training, not a purchase. The server was ready the same day and ran steadily through the whole job."
"Our inference latency dropped after moving to a dedicated GPU, and support helped us pick the right plan instead of upselling."
"Fine-tuning runs overnight without queueing behind other users, and the invoice is a flat monthly figure."
"We rent more GPUs for deadline weeks and release them after delivery."
FAQ
GPU Dedicated Server FAQs
Everything you need to know before you deploy your SAP S/4HANA dedicated server with VyomCloud.
A physical server with one or more NVIDIA GPUs that belong entirely to you, along with dedicated CPU, RAM and storage. The full GPU compute and VRAM are exclusive — not shared or virtualized.
RTX 4090 works well for inference and lighter training. A100 is better when you need 80GB of high-bandwidth memory for serious training. H100 with NVLink is for large-scale multi-GPU training and HPC work. If you’re unsure, start lower and benchmark during the money-back period.
Full dedicated hardware. Exclusive access to the GPU’s VRAM and compute cores.
Yes on the Enterprise tier (4x H100 with NVLink/NVSwitch). Custom Cluster goes further with multi-node setups and InfiniBand.
Single-tenant hardware removes the shared-tenant risk of multi-tenant cloud GPUs. You still handle your own OS security and access controls, the same as any dedicated server.
Yes. Server Migration support is available for moving workloads and datasets from other providers or cloud platforms.
Dedicated means fixed monthly price, always available, no usage spikes and no capacity queues. Cloud instances give more flexibility to scale up and down quickly, but sustained use is often more expensive and popular GPU types can have wait times.
Published tiers start at ₹39,999/month for GPU Starter (RTX 4090) and go up to ₹1,49,999/month for GPU Enterprise (4x H100). Custom clusters are available above that.
Yes. Full root access. Install PyTorch, TensorFlow or anything else you need on top of the pre-installed CUDA stack.
Yes. Moving up the tiers or into a Custom Cluster is supported as your needs grow.
It’s useful for real-world benchmarking. A full multi-week training run won’t finish in 7 days, so plan a representative test job instead.