Large-Scale AI Training
Train heavy neural networks and deep-learning models with high throughput and large memory capacity.
Trusted by 20,000+ Businesses Rent NVIDIA A100 GPUs to Train Large AI Models
Harness 80 GB HBM2e and 2,039 GB/s bandwidth to train AI models and scale multi-GPU workloads instantly.
- 7 MIG Instances
- Enterprise-Grade PCIe GPUs
- On-Demand Access
- Pay-as-You-Go Pricing
80 GB
HBM2e Memory
2039GB/s
Memory Bandwidth
250W
TDP Power
Start With ₹30,000 Free Credits
Train Enterprise-Grade 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 A100 GPU Specifications
VRAM
80 GB HBM2
Peak INT8 Tensor Core
624 TOPS | 1,248 TOPS*
Peak FP64 Tensor Core
19.5 TFLOPS
Decoder
5 (4th Gen)
Why Businesses Choose AceCloud for NVIDIA A100 GPU?
Built from the ground up to deliver performance, agility and deployment speed for AI-driven workloads.
20x Compute Performance
Unlock 20x performance using A100 GPUs, dividing resources across up to 7 simultaneous users.
Structural Sparsity
Accelerate compute throughput and neural network compression with high-bandwidth memory and faster cache performance.
Hassle-Free Integration
Quickly integrate GPUs into existing infrastructure to boost parallel compute power without complexity.
Scalable Infrastructure
Create scalable storage volumes with built-in replication to ensure high availability and minimize downtime risks.
A100: Balanced Performance for LLMs, Vision and HPC
Strong TFLOPS, generous VRAM and efficient utilization across mixed workloads.
Turn AI Ambitions Into Real-World Results
NVIDIA A100 powers models that train 10× faster and scale seamlessly across clusters - so your ideas reach production faster.
Up to 20× Speed
Faster Training
2 TB/s Bandwidth
Real-Time Data
Multi-Workload Efficiency
Seamless Scaling
No waiting. No wasted power. Just breakthrough performance that grows with you.
Where NVIDIA A100 GPUs Shine at Scale
Built for heavy AI, data, and compute workloads powerful enough for training, analytics, or simulation at large scale.
High-Volume AI Inference
Serve multiple models in production with low latency and high throughput from NLP to vision tasks.
Big Data Analytics & ML Pipelines
Accelerate data analytics, ETL jobs, and large-scale machine learning pipelines with GPU-powered compute and memory.
HPC & Scientific Simulations
Run simulations, scientific computations, and data-intensive workloads harnessing A100’s compute and memory bandwidth.
Multi-Tenant / Multi-Instance Workloads
Using virtualization (MIG), split A100 into multiple instances to serve different jobs/users concurrently improving utilization.
Mixed-Workload Flexibility
Run training, inference, analytics, or batch jobs on the same infrastructure A100 adapts to varying compute needs.
GPU-Accelerated Data Center & Cloud Apps
Deploy A100 in data-center setups for scalable AI & HPC services ideal for cloud providers, enterprise AI stacks, and large-scale compute clusters.
Your Custom Solution
Have heavy AI, data, or HPC workloads? We’ll help you build the right A100-powered setup for your needs.
Configure Your A100 Setup
Ready to Scale From Prototype to Production?
Deploy A100 GPUs instantly and cut training costs by up to 50% with efficient, scalable compute.
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 A100 is a data center GPU with up to 80 GB HBM2e memory and very high bandwidth, built for large-scale AI, data analytics and HPC workloads that outgrow consumer or mid-range GPUs.
Yes. A100 is widely used for training and fine-tuning LLMs and other transformer models because its memory size and Tensor Cores handle large batches, long sequences and complex architectures efficiently.
On AceCloud you use A100 80 GB HBM2e GPUs with very high memory bandwidth (around 2 TB/s), which helps keep large models and datasets on the GPU and reduces bottlenecks during training and inference.
Yes. A100 accelerates high-volume NLP and vision inference and is also suitable for HPC workloads such as simulations, risk modeling and scientific computing.
You don’t buy A100 hardware; you launch A100-powered virtual machines, run your jobs and pay based on the configuration and time you use. You can increase or reduce A100 capacity as your workload changes.
Yes. You can spin up A100 instances for a PoC, experiment or short training run, then scale down or shut them off when you are done so you’re not paying for idle GPUs.
A100 pricing follows a pay-as-you-go model with hourly and monthly rates shown on the A100 pricing pages. Your cost depends on GPU count, vCPUs, RAM, storage and region.
Yes. With Multi-Instance GPU (MIG), a single A100 can be split into several isolated GPU instances, each with its own compute and memory, so you can run multiple services or tenants on the same card.
Yes. You can choose nodes with multiple A100 80 GB GPUs and then scale across nodes using Kubernetes or AceCloud GPU clusters for distributed training or large inference fleets.
A100 instances work with common stacks such as PyTorch, TensorFlow, JAX, RAPIDS, CUDA, cuDNN, TensorRT and Triton Inference Server, either from AceCloud images or your own containers and IaC.
New customers typically receive free credits, shown on the A100 and pricing pages, so they can benchmark A100 for their workloads before moving to longer-term plans.
