NVIDIA A100 Cloud GPU
Compare NVIDIA A100 80GB specifications, form factors and workload fit before choosing a cloud GPU provider.
NVIDIA Ampere data-center GPU
A versatile GPU for AI, analytics and HPC
NVIDIA A100 is an Ampere-generation data-center GPU designed for artificial intelligence, accelerated data analytics and high-performance computing. The A100 80GB combines HBM2e memory, third-generation Tensor Cores, Multi-Instance GPU and NVLink in PCIe and SXM form factors.
- AmpereGPU architecture
- 80 GBHBM2e GPU memory
- 2.04 TB/sSXM memory bandwidth
- 600 GB/sNVLink bandwidth
Who should choose an A100 cloud GPU?
Choose A100 when you need a mature, broadly supported accelerator for AI training, inference, analytics or FP64-based HPC. It can offer better value than newer GPUs when 80 GB is sufficient and Hopper- or Blackwell-specific capabilities are not required.
NVIDIA A100 80GB specifications
NVIDIA offers the A100 80GB in PCIe and SXM form factors. Their published compute specifications are the same, while memory bandwidth, power, physical design and multi-GPU deployment options differ.
| Specification | A100 80GB PCIe | A100 80GB SXM |
|---|---|---|
| Architecture | NVIDIA Ampere | NVIDIA Ampere |
| GPU memory | 80 GB HBM2e | 80 GB HBM2e |
| Memory bandwidth | 1,935 GB/s | 2,039 GB/s |
| FP64 | 9.7 TFLOPS | 9.7 TFLOPS |
| FP64 Tensor Core | 19.5 TFLOPS | 19.5 TFLOPS |
| FP32 | 19.5 TFLOPS | 19.5 TFLOPS |
| TF32 Tensor Core* | 156 TFLOPS | 156 TFLOPS |
| BF16 / FP16 Tensor Core* | 312 TFLOPS | 312 TFLOPS |
| INT8 Tensor Core* | 624 TOPS | 624 TOPS |
| Maximum TDP | 300 W | 400 W standard configuration |
| MIG | Up to 7 instances at 10 GB each | Up to 7 instances at 10 GB each |
| Form factor | Dual-slot air cooled or single-slot liquid cooled PCIe | SXM |
| Interconnect | Two-GPU NVLink bridge at 600 GB/s; PCIe Gen4 at 64 GB/s | NVLink at 600 GB/s; PCIe Gen4 at 64 GB/s |
* Tensor Core performance doubles with sparsity: TF32 up to 312 TFLOPS, BF16 and FP16 up to 624 TFLOPS, and INT8 up to 1,248 TOPS. NVIDIA also notes that selected custom-thermal-solution A100 80GB SXM systems can support TDPs up to 500 W.
A100 advantage
Choose A100 for maturity and workload breadth
A100 supports AI training, inference, accelerated analytics and FP64-based HPC on one platform. It is widely supported across CUDA software, enterprise systems and cloud environments.
When to move beyond A100
Newer GPUs offer more memory or newer precision formats
H100 adds Hopper Transformer Engine and FP8, H200 increases memory to 141 GB, and B200 adds Blackwell FP4. These options may be stronger when model scale, context length or maximum throughput matters more than A100 availability and price.
What makes A100 useful?
Open each capability for a practical explanation of its effect on cloud AI, analytics and HPC workloads.
Third-generation Tensor CoresMixed-precision acceleration for AI
A100 supports TF32, BF16, FP16 and INT8 Tensor Core workloads alongside FP32 and FP64 compute. This allows one GPU platform to support AI training, inference and scientific computing.
Multi-Instance GPUPartition one GPU into isolated instances
One A100 80GB GPU can be divided into as many as seven hardware-isolated MIG instances, each with 10 GB of GPU memory, cache and compute resources.
NVLink and NVSwitch scalingConnect GPUs for larger workloads
A100 supports up to 600 GB/s of NVLink bandwidth. SXM GPUs can be connected through HGX A100 platforms, while PCIe GPUs support a two-GPU NVLink bridge.
High-bandwidth HBM2e memoryUp to 80 GB for large models and datasets
The A100 80GB provides HBM2e memory with up to 2,039 GB/s bandwidth on the SXM version, helping accelerate memory-intensive AI, analytics and HPC workloads.
Structural sparsityHigher throughput for sparse AI models
A100 Tensor Cores can use structured sparsity to provide up to twice the performance for supported sparse models, especially for inference and selected training workloads.
Broad precision supportOne accelerator for AI and HPC
A100 supports FP64, FP64 Tensor Core, FP32, TF32, BF16, FP16 and INT8 formats, allowing teams to choose the precision that best fits performance and accuracy requirements.
Best workloads for an A100 cloud GPU
AI training and fine-tuning
Suitable for deep learning training, model fine-tuning and distributed workloads that benefit from Tensor Cores, large memory and multi-GPU scaling.
AI inference
A practical option for high-throughput inference using FP16 or INT8, with MIG support for sharing one GPU across several isolated workloads.
HPC and scientific computing
Designed for simulations, computational science and research workloads that need FP64 performance, high memory bandwidth and mature CUDA support.
Accelerated data analytics
Useful for RAPIDS, Spark and other GPU-accelerated analytics workloads that process large datasets and benefit from high-bandwidth memory.
A100 is a strong fit when…
- You need a mature data-center GPU for AI training, inference or HPC.
- Your workload fits within 80 GB of GPU memory.
- You need MIG to divide one GPU among isolated users or applications.
- Your workload benefits from FP64, TF32, BF16, FP16 or INT8.
- You need NVLink for two-GPU or larger HGX configurations.
- You want broad software and cloud-provider availability.
Consider another GPU when…
A100 may not be the best option when your workload needs FP8, more than 80 GB of memory or the highest available transformer throughput.
- Compare H100 for FP8 and stronger transformer performance.
- Compare H200 when 141 GB of memory and higher bandwidth matter.
- Compare L40S for smaller inference and visual AI workloads.
Provider comparison
Cloud providers offering NVIDIA A100
Compare A100 memory size, instance resources, deployment region, starting price and purchasing options. Check whether each listing uses A100 40GB or A100 80GB before comparing costs.
| Provider | A100 offering | GPU variant | Example configuration | Regions | Starting price | Billing options | Details |
|---|---|---|---|---|---|---|---|
| AceCloud | 1× NVIDIA A100 80GB | 80 GB HBM2e | 16 vCPU · 128 GB RAM | India and United States | ₹125/hr on-demand · ₹90,000/month | Hourly · monthly · 6/12-month plans · selected spot | View provider |
| E2E Networks | 1× NVIDIA A100 | 40 GB or 80 GB | 80GB plan: 16 vCPU · 115 GB RAM | India | ₹189/hr · ₹99,250/month for 80GB | On-demand · monthly · 6 months · annual · spot subject to capacity | View provider |
| Utho | NVIDIA A100 80GB | 80 GB HBM2e | Custom configuration | India | Custom quote | On-demand · reserved capacity · spot to confirm | View provider |
| Cyfuture AI | 1× NVIDIA A100 80GB | 80 GB HBM2e | 8 vCPU · 64 GB RAM | India, United States and Europe | From $2.20/hour on-demand | On-demand · 6-month · annual | View provider |
| Microsoft Azure | NCads A100 v4 | 1–4× A100 80GB PCIe | 1-GPU size: 24 vCPU · 220 GB RAM | Selected Azure regions | Use Azure Pricing Calculator | Pay as you go · reservations · savings plan · Spot | View provider |
| DigitalOcean | Paperspace A100 | 40 GB or 80 GB | 12 vCPU · 90 GiB RAM | Selected Paperspace regions | $3.09/hr for A100 · $3.18/hr for A100-80G | Hourly on-demand · single- and 8-GPU options | View provider |
| AWS | EC2 P4d / P4de | 8× A100 40GB or 8× A100 80GB | 96 vCPU · 1,152 GiB RAM | Selected AWS regions | Use AWS Pricing Calculator | On-Demand · Savings Plans · reservations · Spot · Capacity Blocks | View provider |
Compare the complete instance rather than only the displayed GPU price. Providers may offer different A100 memory sizes, form factors, GPU counts, vCPU, RAM, storage, networking, regions, commitment terms and tax treatment. Spot capacity can be interrupted and may not be available for every configuration.
NVIDIA A100 FAQs
How much GPU memory does NVIDIA A100 have?
The A100 configuration covered here has 80 GB of HBM2e memory in both PCIe and SXM form factors.
What is the memory bandwidth of NVIDIA A100?
The A100 80GB PCIe provides 1,935 GB/s, while the A100 80GB SXM provides 2,039 GB/s.
Is NVIDIA A100 suitable for LLM training?
Yes. A100 supports Tensor Core training with TF32, BF16 and FP16, and its 80 GB memory can support many training and fine-tuning workloads.
Does NVIDIA A100 support MIG?
Yes. One A100 80GB GPU can be partitioned into up to seven isolated MIG instances with 10 GB of memory each.
What is the difference between A100 PCIe and A100 SXM?
Both provide 80 GB HBM2e and the same published compute specifications. SXM has higher memory bandwidth, a 400 W standard TDP and broader NVLink scaling, while PCIe uses a dual-slot or liquid-cooled PCIe form factor with a 300 W TDP.
Is A100 still a good cloud GPU?
A100 remains useful for mature AI, analytics and HPC workloads when its performance, 80 GB memory, MIG support and pricing fit the deployment better than newer Hopper or Blackwell GPUs.
Compare A100 configurations before choosing a provider
Confirm the memory size and form factor first. Then compare GPU count, NVLink and MIG access, CPU, RAM, storage, networking, region and the complete hourly or monthly price.