Large-Scale AI Training
Train huge deep-learning models or language models quickly H100 makes heavy training feasible.
Trusted by 20,000+ Businesses Rent NVIDIA H100 GPU, Train & Scale Enterprise AI Faster
Train LLMs, run HPC, accelerate inference with Hopper architecture delivering 30× jump in AI throughput over prior gen.
- Up to 30× Faster AI
- Next-Gen Hopper Performance
- Enterprise-Grade Security
- Up to 7 MIG Instances
80 GB
HBM3 Memory
3.35TB/s
Memory Bandwidth
700W
SXM Power
Start With ₹30,000 Free Credits
Unleash AI Compute in Minutes
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 H100 GPU Specifications
VRAM
80 GB
Peak INT8 Tensor Core
3958 TOPS
Peak FP64 Tensor Core
67 TFLOPS
Decoder
7 NVDEC (4th Gen) 7 JPEG
Why Businesses Choose AceCloud for NVIDIA H100 GPUs?
From instant provisioning to optimized infrastructure - AceCloud is built for modern AI workflows.
AI and HPC Excellence
Harness AI and HPC power with tensor cores that ensure rapid, precise processing for complex tasks.
Hopper Architecture
Unlock new enterprise possibilities with the H100, powered by NVIDIA’s advanced and efficient Hopper architecture.
Cost Efficiency
Maximize ROI by streamlining workloads on the H100, achieving exceptional performance with optimized cost efficiency.
Enhanced Security
Protect critical data using secure boot, hardware encryption and tamper-resistant tech built into the H100.
NVIDIA H100: The Benchmark for AI Training and Inference
FP8 Transformer Engine, 80 GB HBM3, NVLink scale for multi-GPU throughput.
The Next Leap in AI Performance.
NVIDIA H100 sets a new benchmark training 30× faster with next-gen Transformer Engine and FP8 precision.
Up to 4 000 TFLOPS
Peak AI Compute
Transformer Engine
Smarter Precision
Scalable NVLink Fabric
Seamless Clustering
No bottlenecks. No limits. Just intelligent compute that redefines what’s possible.
Where NVIDIA H100 Powers Next-Gen AI & HPC
Built for the hardest workloads massive AI training, high-end compute, and data-center scale jobs.
AI Inference & Serving
Serve inference tasks at scale NLP, vision, recommendation systems with top performance and low latency.
HPC Workloads
Run scientific simulations, data-heavy computations, or complex compute workloads that demand strong FP64/FP32 performance.
Big Data & ML Pipelines
Accelerate data-processing, ETL jobs, analytics pipelines and GPU-accelerated data workloads with speed and scale.
MIG Partitioning
Use MIG (multi-instance GPU) to split H100 into smaller, isolated instances ideal for serving multiple workloads/users on one GPU.
Mixed Workload Flexibility
Switch between heavy training, inference, analytics or compute H100 adapts to varied workload demands with high performance.
Data-Center Deployments
Deploy H100 for enterprise-scale AI infrastructure, cloud AI services, or large compute clusters needing maximum efficiency and throughput.
Your Custom Solution
Heavy AI, HPC, or data workloads? Let’s build a tailored H100-based setup optimized for your needs.
Configure Your H100 Setup
Ready to Build What Comes Next in AI?
Rent H100 GPUs instantly and cut training time by up to 30× while reducing power per model.
Create faster. Deliver cleaner. Grow without the hardware headache.
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 H100 is a data center GPU based on the Hopper architecture, designed for large-scale AI, data analytics and HPC. It introduces the Transformer Engine with FP8 support and ships with up to 80 GB of high-bandwidth HBM3/HBM2e memory for very large models and datasets.
H100 is ideal for training and fine-tuning large language models, high-volume LLM inference, recommender systems, computer vision at scale, complex simulations and other HPC workloads that need very high compute and memory bandwidth.
Yes. H100’s Transformer Engine, 4th-gen Tensor Cores and high memory bandwidth make it a common choice for GPT-scale LLMs, long-context models and generative AI workloads, both for training and for low-latency inference in production.
AceCloud provides H100 in HGX configurations with 1×, 2×, 4× or 8× H100 GPUs per node, along with different vCPU and RAM options, so you can start with a single-GPU server and scale to multi-GPU nodes for heavier training or inference clusters.
You don’t buy the H100 hardware. You launch H100-powered instances from the AceCloud console or API, run your jobs and pay based on the instance type and the time it runs, with the option to scale capacity up or down as your workload changes.
Yes. You can spin up H100 instances for PoCs, experiments or short training runs, then shut them down when you’re finished so you’re not paying for idle GPUs between projects. This is useful for benchmarking H100 against other GPUs or validating new models.
H100 instances follow a transparent pay-as-you-go model with published monthly and term pricing by flavor and region. For example, the H100 HGX pricing page lists a 1× H100 80 GB node in India starting around ₹200,000 per month, with 2×, 4× and 8× GPU nodes priced proportionally and discounts for longer terms.
Yes. H100 supports second-generation Multi-Instance GPU (MIG), so you can partition one GPU into several isolated GPU instances, each with its own compute cores and memory. That lets you run multiple services, experiments or tenants on the same H100 with predictable performance.
You can choose multi-GPU HGX nodes (up to 8× H100 per node) and then scale out across nodes using Kubernetes or AceCloud GPU clusters. H100 SXM supports NVLink and NVSwitch for high-bandwidth inter-GPU communication, which is important for large distributed training and massive inference fleets.
H100 instances on AceCloud support popular stacks such as PyTorch, TensorFlow, JAX, RAPIDS, CUDA, cuDNN, TensorRT, Triton Inference Server and other NVIDIA AI Enterprise components. You can start from AceCloud images or bring your own containers and IaC templates.
You can typically launch H100 instances in minutes from the AceCloud console or via API, without ticketing queues or long provisioning delays, and then adjust capacity as your training or inference needs to evolve.
Yes. New AceCloud users usually receive free credits (for example, around ₹20,000 in India or $200 globally for a limited time) that can be applied to H100 and other GPU instances so you can benchmark performance and costs before committing to larger deployments.
Yes. H100 runs in AceCloud’s secure, enterprise-grade data centers with network isolation, encrypted storage options, monitoring and 24/7 support, and is already used for production AI, analytics and HPC workloads in regulated and performance-sensitive environments.
