The NVIDIA H200 pairs the Hopper architecture with 141GB of HBM3e memory and 4.8 TB/s of bandwidth — the capacity headroom that frontier LLM training, long-context inference, and HPC actually need. Run it on Host360's sovereign Indian cloud, billed in INR, with 24x7 SLA-backed support.
From early-stage AI startups to Fortune 500 R&D labs.
MODEL SPECIFICATIONS
The H200 shares the H100's Hopper compute die but nearly doubles usable memory. That single change removes the ceiling on the workloads teams care about most: fitting bigger models on fewer GPUs and keeping high-throughput inference from stalling on memory bandwidth.
*Train and fine-tune foundation models and 100B+ parameter LLMs *Serve long-context inference with large batch sizes & KV caches *Accelerate scientific simulation and engineering workloads *Run large-scale analytics and data processing pipelines *Scale distributed, multi-GPU training over NVLink *Enable rendering and visualization projects.
Train advanced machine learning models.
Match NVIDIA H200 infrastructure to workload requirements, operational priorities, and business objectives.
| Feature | AI Development ₹160 / hr | Production AI ₹78,000 / month | Research Computing Custom — contact sales |
|---|---|---|---|
| Main Users |
The GPU is the same everywhere. What changes your economics, compliance posture, and time-to-production is who runs it and where.
Every H200 workload runs inside Indian data centres — full data residency, no cross-border exposure. Built for BFSI, healthcare, and government.
INR-denominated pricing with no forex risk and no egress surprises. Materially lower GPU compute cost than AWS, Azure, and GCP.
PyTorch, TensorFlow, CUDA, Jupyter, and LLM frameworks pre-configured. Your team ships from day one — no setup overhead.
On-demand cloud GPU for burst workloads or dedicated bare-metal for sustained training — same portal, same support.
200+
Indian operators
99.99%
Monthly uptime
24/7
Named SRE
Frequently asked
Learn about NVIDIA H200 GPU infrastructure, deployment options, and enterprise AI computing environments.
NVIDIA H200 is a data center GPU based on the NVIDIA Hopper architecture and designed for AI, HPC, analytics, and accelerated computing workloads.
NVIDIA H200 supports AI training, AI inference, scientific computing, analytics, simulation, and visualization workloads.
AI startups, enterprises, research teams, ML engineers, data scientists, and HPC users commonly deploy NVIDIA H200 environments.
Yes. NVIDIA H200 is commonly used for training, fine-tuning, and deploying large language model workloads.
Yes. Research organizations and engineering teams use NVIDIA H200 for simulations, modeling, and computational research.
Yes. NVIDIA H200 can be provisioned through cloud GPU infrastructure for on-demand AI and HPC workloads.
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Accelerate LLM training, production inference, and scientific computing on India's sovereign GPU cloud. Talk to a solutions architect and get a configuration sized to your models.
Deploy production AI inference services.
Accelerate simulation workloads.
Support visual computing tasks.
| ML Engineers |
| AI Product Teams |
| Research Teams |
| Typical Applications | AI Models | AI Services | Simulations |
| Workflow Type | Development Cycles | Live Operations | Research Projects |
| Resource Usage | Training Jobs | Inference Requests | Compute Tasks |
| Environment Focus | Experimentation | Availability | Computation |
| Team Objectives | Model Improvement | User Experience | Research Outcomes |
| Deployment Type | — | — | — |
India-based cloud engineers who understand AI workloads, with named SRE contacts and fast escalation — not generic ticketing.
RBAC, encryption in transit and at rest, VPC isolation, audit logging, and DPDP Act 2023 readiness by default.
“We serve diagnostic image inference on Mumbai BOM-1. p99 latency 62ms, INR-billed at ₹0.18 per 1k tokens. The team won't touch a hyperscaler again.”
Neha Kapoor
“Genomics workloads burst hard. HOST360's reserved + on-demand mix means our 12-month committed base sits under 30% of spend, the rest spikes only when we sequence.”
Aarav Mehta
“We serve diagnostic-image inference from Mumbai on H200. Long-context batches that used to stall now run clean at p99 62ms. The data-residency posture answered our board's compliance questions in one call.”
Rohan Desai
“Genomics workloads burst hard. A reserved H200 base plus on-demand spikes keeps our committed spend under 30% of the bill — we only pay for the peaks when we sequence.”