Why CloudPe
H200 on our cloud vs global hyperscalers. Built for India, priced for scale.
Built for India
Lower latency to users in India and easier compliance with data-residency requirements.
Transparent pricing
Flat ₹300/hr on-demand for 1× H200 with generous CPU/RAM. No confusing credit systems.
Full GPU stack
Move workloads between H200, L4 and RTX Pro 6000 on the same platform, so you can match the GPU to the job instead of overpaying for headroom you don't need.
Human support
Direct access to GPU and infrastructure specialists in India who understand AI workloads.
Technical specifications
Full hardware specifications for the NVIDIA H200 Tensor Core GPU on CloudPe.
How H200 compares to H100 and A100
Most teams evaluating H200 are already running H100 or A100 GPUs somewhere, in their own datacenter or with another provider. This section shows what actually changes if you move to H200.
| Metric | H200 | H100 | A100 |
|---|---|---|---|
| GPU memory | 141 GB HBM3e | 80 GB HBM3 | 80 GB HBM2e |
| Memory bandwidth | 4.8 TB/s | 3.35 TB/s | ~2.0 TB/s |
| FP16 Tensor Core (with sparsity) | 1,979 TFLOPS | 1,979 TFLOPS | 624 TFLOPS |
| NVLink bandwidth | 900 GB/s | 900 GB/s | 600 GB/s |
Why compute is identical between H200 and H100
H200 and H100 use the same Hopper GPU die, so raw Tensor Core throughput is the same on both. The difference is memory: H200 has 76% more capacity and 43% more bandwidth. That extra headroom is what improves real-world inference throughput for large models. It is not a change in raw compute speed, and any claim that says otherwise should be corrected.
Why A100 shows a bigger jump
A100 is a generation behind Hopper. It has lower Tensor Core throughput and older HBM2e memory. Moving from A100 to H200 improves both compute and memory at once, which is why that gap is larger than the H100 comparison.
When to choose H200 over H100 or A100
Choose H200 when your workload is memory-bound rather than compute-bound: long-context inference above 100K tokens, large-batch training, or models that need to run without sharding across GPUs. If your model fits comfortably in 80GB and you are not bandwidth-limited, H100 may offer a better price for the job.
Key performance benchmarks
Here are some benchmarks to look at for H200 GPU cloud performance.
Llama 2 70B inference
NVIDIA reports up to 2x higher inference throughput on Llama 2 70B compared to H100, driven by the larger batch sizes H200's extra memory allows (NVIDIA preliminary measured performance, subject to change).
GPT-3 175B inference
NVIDIA's published inference benchmarks show batch size doubling, from 64 to 128, for GPT-3 175B on an 8-GPU H200 system compared to an 8-GPU H100 system, which translates directly into higher throughput per system (NVIDIA preliminary measured performance, subject to change).
Who is H200 perfect for?
From startups to enterprises, H200 powers demanding AI workloads.
AI product companies and SaaS
Multi-tenant LLM APIs with strict latency SLOs. Chatbots, copilots, and agents with 100K+ token context. RAG over large vector stores.
Fintech, BFSI and analytics
Real-time risk engines and fraud detection. High-throughput inference over financial documents. Compliance-sensitive workloads needing India data residency.
Enterprises and system integrators
Private LLMs on internal data (HR, legal, sales). Hybrid deployments mixing on-prem and cloud GPUs. AI transformation proofs of concept where performance is non-negotiable.
Research and academia
Training and fine-tuning open-source LLMs and VLMs. Large-scale simulation and HPC. Scientific computing at scale.
H200 instance pricing
Pick the configuration that fits your workload. All plans run on Tier-4 datacenter infrastructure in India.
| Config | GPU | vCPU | RAM | Storage | Hourly | Monthly |
|---|---|---|---|---|---|---|
| NVIDIA H200 | 1× H200 | 64 vCPU | 256 GB | 30 GB | ₹318.68 | ₹2,09,372 |
Built on NVIDIA Hopper architecture
The engineering behind H200's memory and throughput advantages.
Fourth-generation Tensor Cores and Transformer Engine
H200's Tensor Cores use FP8 precision to speed up transformer-based training and inference, giving a real uplift for large language models compared to prior-generation hardware.
NVLink and NVSwitch interconnect
High-bandwidth GPU-to-GPU interconnect lets workloads scale efficiently across multi-GPU nodes. This matters most for training jobs that span more than one H200.
Multi-instance GPU (MIG) partitioning
Split a single H200 into up to 7 isolated GPU instances, each with dedicated memory and compute. Useful for running several smaller workloads, or serving multiple tenants, on shared hardware without one job slowing another down.
Explore other GPUs on CloudPe
Not sure H200 is the right fit? Compare it against the rest of the CloudPe GPU lineup.
NVIDIA RTX Pro 6000
Built for 3D rendering, generative media, and visual AI work, with 96 GB GDDR7 on Blackwell architecture.
View RTX Pro 6000Frequently asked questions
CloudPe prices H200 instances transparently, with a flat hourly rate and no hidden credit systems. You can estimate your exact cost using the pricing calculator on this page, or check the instance pricing table above for standard single- and multi-GPU configurations.
Yes. Alongside on-demand hourly billing, CloudPe offers monthly and long-term reserved pricing for workloads that run continuously. Reserved plans typically cost less per hour than on-demand, making them a better fit for sustained training or production inference workloads rather than short, occasional jobs.
Yes. CloudPe supports 2x, 4x, and 8x H200 configurations for large-scale AI training and inference workloads. You can launch multiple H200-backed instances and coordinate them yourself, or provision a dedicated multi-GPU node directly through the CloudPe dashboard. See the instance pricing table above for details.
Yes. CloudPe lets you run different GPU types side by side depending on what each part of your workload needs. For example, you could train on H200 for the memory headroom, then serve inference on a more cost-efficient L4 instance once the model is ready.
H200 availability depends on current capacity in each CloudPe data center in India, and coverage is expanding as demand grows. Check the CloudPe dashboard or contact a GPU specialist for the most current availability before you plan a deployment or commit to a project timeline.
H200 is built for workloads that are limited by memory or bandwidth rather than raw compute: large-scale AI training, long-context LLM inference, retrieval-augmented generation over big vector stores, and HPC or scientific computing tasks that move large amounts of data between memory and compute.