Generative AI & LLMs
Run or fine-tune huge language models and generative AI systems with large memory and high throughput.
Trusted by 20,000+ Businesses Rent NVIDIA H200 GPUs for Next-Gen AI Scale
Train trillion-parameter LLMs and GenAI models in days, not weeks with on-demand H200 clusters from AceCloud.
- Seamless Multi-GPU Scaling
- 60% Cheaper than AWS
- Mixed-Precision Tensor Cores
- Pay-as-You-Go
141 GB
HBM3e Memory
4.8TB/s
Memory Bandwidth
1.9x Faster
AI Training vs H100
Start With ₹30,000 Free Credits
Train Next-Gen AI Without the Wait
Deploy in minutes and start running AI workloads instantly.
- Enterprise-Grade Security
- Instant Cluster Launch
- 1:1 Expert Guidance
Your data is private and never shared with third parties.
NVIDIA H200 GPU Specifications
VRAM
141 GB HBM3e
Decoder
7 NVDEC/ 7 JPEG
FP64 Tensor Core
67 TFLOPS
INT8 Tensor Core
3958 TFLOPS
Why Businesses Choose AceCloud for NVIDIA H200 GPUs?
AceCloud delivers AI without friction — from lightning-fast provisioning to infrastructure fine-tuned for GenAI, LLMs, and HPC at scale.
AI and HPC Excellence
Harness AI and HPC power with tensor cores ensure rapid, precise processing for complex tasks.
Hopper Architecture
Unlock enterprise innovation with H200, built on NVIDIA’s enhanced Hopper architecture and HBM3e memory.
Cost Efficiency
Maximize ROI with H200’s higher throughput and memory bandwidth for superior performance and cost efficiency
Enhanced Security
Safeguard data with H200’s secure boot, hardware-level encryption, and enhanced tamper-resistant architecture.
NVIDIA H200: Higher Bandwidth, More Memory, Better Throughput
HBM3e, expanded VRAM and improved FP8 compute for next-generation AI scaling.
Where NVIDIA H200 Powers High-End AI & HPC
Built for big models, heavy compute and intense AI workloads when you need power without compromise.
Scalable AI Inference
Serve NLP, vision, embeddings, or multimodal models at scale fast and efficient.
HPC & Scientific Compute
Handle simulations, research computing, and data-intensive tasks that demand strong precision and compute power.
Large-Scale Model Training
Train deep learning and large neural-net models with enough memory and bandwidth for big datasets.
Multi-Instance GPU Use
Split the GPU for different workloads or users, improving utilization across teams or services.
Cloud-Scale AI Systems
Deploy H200 in cloud racks or enterprise servers for scalable, production-ready AI workloads.
Mixed Workload Environments
Run training, inference, analytics, or HPC tasks on the same GPU without sacrificing performance.
Your Custom Solution
Working on big AI or HPC projects? We’ll help you build an H200-powered system that fits your workload.
Configure Your H200 Setup
Trusted by Industry Leaders
See how businesses across industries use AceCloud to scale their infrastructure and accelerate growth.
Ravi Singh
Tagbin
“We moved a big chunk of our ML training to AceCloud’s A30 GPUs and immediately saw the difference. Training cycles dropped dramatically, and our team stopped dealing with unpredictable slowdowns. The support experience has been just as impressive.”
60% faster training speeds
Dheeraj Kumar Mishra
“We have thousands of students using our platform every day, so we need everything to run smoothly. After moving to AceCloud’s L40S machines, our system has stayed stable even during our busiest hours. Their support team checks in early and fixes things before they turn into real problems.”
99.99*% uptime during peak hours
Jaykishan Solanki
“We work on tight client deadlines, so slow environment setup used to hold us back. After switching to AceCloud’s H200 GPUs, we went from waiting hours to getting new environments ready in minutes. It’s made our project delivery much smoother.”
Provisioning time reduced 8×
Frequently Asked Questions
The NVIDIA H200 is a data center GPU based on the Hopper architecture with 141 GB of HBM3e memory and up to 4.8 TB/s memory bandwidth. It is designed to accelerate generative AI, large language models and HPC workloads that are limited by memory capacity and bandwidth on earlier GPUs.
Compared to H100, H200 doubles memory capacity (141 GB vs 80 GB) and increases memory bandwidth to about 4.8 TB/s, which helps with larger models and longer context windows. While A100, H200 adds the Hopper Transformer Engine and much larger and faster HBM, giving a bigger step up for modern LLM and generative AI workloads.
H200 is ideal for LLM training and fine-tuning, long-context LLM inference, RAG systems, multimodal and generative AI, recommendation engines, large graph workloads and memory-heavy HPC simulations.
A typical NVIDIA H200 configuration provides 141 GB HBM3e, about 4.8 TB/s memory bandwidth, Hopper Tensor Cores with FP8/FP16/BF16 support and FP64 for HPC and is available in SXM or PCIe/NVL form factors for data center servers.
On AceCloud you do not buy H200 hardware. You launch H200 GPU instances from the console or API, run your workloads and pay based on GPU count, configuration and usage time, with flexible hourly and monthly plans instead of long-term infrastructure lock-in.
Yes. You can spin up H200 instances for PoCs, benchmarks or short training runs, then shut them down when you are done so you are not paying for idle GPUs between projects. This suits teams that want to validate H200 for LLMs or HPC before scaling.
Renting H200 on AceCloud turns GPU spend into an operating expense instead of a large upfront purchase. You can start with a small cluster, scale up or down as model sizes and traffic change, test new architectures without buying new hardware and avoid the ongoing cost of data center space, power, cooling and maintenance.
Yes. The combination of 141 GB HBM3e and 4.8 TB/s bandwidth is designed to keep large models and long context windows in GPU memory, reducing off-chip traffic and improving throughput for LLM training and inference, especially when you push context lengths and batch sizes higher.
H200 targets both AI and HPC; its Hopper Tensor Cores, FP64 support and very high memory bandwidth make it well suited for simulations, scientific workloads and other data-intensive HPC tasks where memory movement is a bottleneck.
Yes. H200 supports Multi-Instance GPU (MIG), so a single H200 can be split into up to seven isolated GPU instances with dedicated memory and compute, which lets you run multiple services or tenants on one card with predictable performance and better utilization.
You can use the standard NVIDIA Hopper stack: PyTorch, TensorFlow, JAX, CUDA, cuDNN, TensorRT, Triton Inference Server and other NVIDIA AI Enterprise components, as well as common data and HPC frameworks, either from AceCloud images or your own containers and IaC templates.
You can usually launch H200 instances in minutes through the AceCloud console or APIs, with automated provisioning and no long manual setup, so you can move from evaluation to running real workloads quickly.
Yes. H200 runs in AceCloud’s secure, enterprise-grade cloud with transparent pricing, Indian and global regions, and 24/7 support, and is already positioned for production grade AI, LLM and HPC deployments where reliability and cost control matter.
