Production-grade Hopper compute for teams training LLMs, running inference at scale, and pushing HPC workloads — deployed in Indian data centres, billed in INR, at 30–40% below hyperscaler rates.
From early-stage AI startups to Fortune 500 R&D labs.
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PLATFORM CHARACTERISTICS
The H100 is NVIDIA's Hopper-generation data-center GPU, built for transformer-heavy AI. Its FP8 Transformer Engine delivers up to 4× faster large-model training and up to 30× faster inference than the A100 — which means shorter training runs, lower cost per token, and models in production sooner. Host360 delivers that hardware inside India, on infrastructure your compliance team can actually sign off on.
Enterprise GPU Platform
Centralize H100 capacity across teams without buying hardware or waiting on procurement. Host360 gives platform and DevOps teams governed, on-demand GPU resources — with the AI stack pre-installed so engineers ship on day one.
*Centralize GPU capacity for multiple teams and projects *Allocate, isolate, and reclaim resources on demand. *Simplify infrastructure planning and allocation. *Role-based access, private networking, and full audit logs *Scale from a single GPU to multi-node NVLink clusters
Support shared environments.
Separate resources by project.
Monitor infrastructure usage.
Control resource permissions.
Select an infrastructure approach based on organizational structure, operational requirements, and resource management preferences.
| Feature | Dedicated Environment ₹160 / hr | Shared Platform ₹78,000 / month | Managed Operations Custom — contact sales |
|---|---|---|---|
| Administration | Customer Managed | Shared Governance | Managed Service |
| Resource Allocation | Fixed Allocation | Shared Resources | Managed Capacity |
| User Access | Restricted Users | Team Access | Controlled Access |
| Operational Focus | Direct Control | Collaboration | Simplified Operations |
| Management Style | Independent | Coordinated | Service-Led |
| Governance | Team Policies | Shared Policies | Managed Policies |
| Deployment Type | — | — | — |
40% avg cloud cost reduction · 3× faster AI deployment · 99.99% uptime · 60% less ops overhead.
Every H100 workload runs inside Indian data centres. Full data residency, no cross-border exposure. Built for BFSI, healthcare, and government.
INR pricing, no forex risk, no egress surprises vs. AWS, Azure, and GCP.
PyTorch, TensorFlow, CUDA, Jupyter, and LLM frameworks pre-configured. Zero setup overhead.
On-demand cloud for bursts, dedicated bare-metal for sustained runs — same portal, same support.
RBAC, encryption at rest and in transit, VPC isolation, audit logs, DDoS protection.
Supported by cloud engineers who understand AI workloads, not generic ticket queues.
INFRASTRUCTURE RESOURCES
Discover infrastructure services that help organizations build, connect, secure, and manage enterprise computing environments.
STORAGE
Centralize file access and collaboration across teams, applications, and enterprise environments.
NETWORKING
Create isolated cloud environments with dedicated networking and resource segmentation.
CONNECTIVITY
Enable secure communication between infrastructure resources and internal services.
SECURITY
Control network access through centralized traffic filtering and security policies.
OPERATIONS
Maintain consistent network endpoints for applications, services, and infrastructure.
NETWORKING
Distribute traffic across resources and support application delivery architectures.
200+
Indian operators
99.99%
Monthly uptime
24/7
Named SRE
Frequently asked
Learn about NVIDIA H100 infrastructure, deployment models, operational considerations, and enterprise GPU environments.
NVIDIA H100 is a data center GPU designed for accelerated computing, enterprise AI infrastructure, and high-performance computing environments.
Enterprises, AI startups, research institutions, cloud providers, engineering organizations, and data-driven businesses commonly deploy NVIDIA H100 environments.
Yes. Organizations can deploy NVIDIA H100 infrastructure in dedicated, shared, or managed cloud environments based on operational requirements.
Yes. NVIDIA H100 is widely adopted for large-scale computing environments that require enterprise-grade GPU resources.
Yes. Organizations often implement shared GPU environments to support multiple projects, departments, and user groups.
Yes. NVIDIA H100 deployments can operate alongside storage, networking, security, and cloud management services.
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Access H100 GPU capacity for training, inference, and HPC — on sovereign Indian cloud, with the AI stack ready and engineers on call.
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