A100 SXM4 80GB on AWS
Visit AWSAWS's NVIDIA A100 SXM4 80GB GPU offering, powered by EC2 P4de instances, stands out for demanding AI/ML workloads requiring high memory bandwidth and capacity. Each GPU delivers 80GB HBM2e VRAM, enabling training and inference of massive models like GPT-scale LLMs, scientific simulations, and genomics analysis without excessive sharding. As the cloud leader, AWS provides seamless integration with SageMaker for fully managed ML pipelines, S3 for petabyte-scale data, and EKS for Kubernetes-orchestrated clusters. Key value propositions include per-second billing for flexibility, Spot Instances slashing costs by up to 90%, and global availability across 100+ Availability Zones for high resilience. This combination targets large enterprises and research orgs prioritizing ecosystem depth, scalability, and reliability over raw price. With NVLink and Elastic Fabric Adapter (EFA), it excels in multi-node distributed training, offering production-grade performance for mission-critical applications.
Why NVIDIA A100 SXM4 80GB on AWS?
Opt for AWS with NVIDIA A100 SXM4 80GB for unparalleled ecosystem integration: SageMaker handles end-to-end ML from notebooks to deployment, while EKS and Batch simplify orchestration. P4de instances pair the GPU's 80GB VRAM and Ampere architecture (624 TFLOPS Tensor FP16) with 600 GB/s NVLink intra-node and 400 Gbps EFA inter-node bandwidth, enabling efficient scaling to thousands of GPUs. Per-second billing and Spot Instances optimize costs for bursty workloads, unlike fixed-hour competitors. Global infrastructure ensures <1% downtime, complementing the GPU's enterprise reliability. Ideal when deep ties to AWS services like Glue, Redshift, or Trainium outweigh isolated GPU pricing.
Live Pricing
Real-time NVIDIA A100 SXM4 80GB offers from AWS
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 96 vCPU 1152GB RAM | Virginia | $5.12/GPU/hr $40.97/hr total (8×) |
Performance Notes
In AWS P4de.24xlarge (8x A100 80GB), expect NVIDIA-spec performance: 19.5 TFLOPS FP64, 312 TFLOPS FP32, 2,496 TFLOPS Tensor FP16 w/ sparsity. NVLink delivers 600 GB/s GPU-to-GPU; EFA provides 3,200 Gbps aggregate for clusters, with NCCL scaling near-linear to 8 GPUs/node per AWS MLPerf benchmarks. Storage options include 100 Gbps EBS gp3 or FSx Lustre (exabytes, sub-ms latency). Multi-node training shines for ResNet/DLRM, but perf varies by framework (PyTorch/TensorFlow 2.10+ CUDA 11.8). Known strengths: excellent for memory-bound LLMs; limitations: PCIe-attached storage may bottleneck I/O-heavy jobs without tuning.
The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.
Best For
Unique Features
- Proprietary silicon like Trainium and Inferentia chips
- Fully managed ML development environment with SageMaker
VRAM
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA A100 SXM4 80GB on AWS involves launching EC2 P4de instances via console, CLI, or SageMaker. Use Deep Learning AMIs preloaded with CUDA 11.8, cuDNN, and frameworks for instant productivity. Scale from single GPU to clusters effortlessly.
Steps
- 1Log into AWS Management Console and navigate to EC2 Dashboard.
- 2Click 'Launch Instance'; search and select 'p4de.24xlarge' under GPU instances.
- 3Choose 'Deep Learning AMI GPU PyTorch' or TensorFlow; configure EBS volume (min 500GB gp3).
- 4Set security group (SSH port 22), launch key pair, and review/launch instance.
- 5SSH in: `ssh -i key.pem ubuntu@ec2-ip`; verify GPUs with `nvidia-smi` and run benchmarks.
Pro Tips
- Request Spot Instances via EC2 console for up to 90% savings on interruptible ML training jobs.
- Use SageMaker Distributed Training with EFA for automatic multi-node scaling and fault tolerance.
- Enable instance metadata for NVIDIA drivers; tune NCCL for NVLink/EFA to maximize multi-GPU throughput.
Frequently Asked Questions
What is AWS's billing model for NVIDIA A100 SXM4 80GB?▾
AWS bills per-second for GPU instances including NVIDIA A100 SXM4 80GB. Per-second billing ensures you only pay for exactly the compute time you use, which is particularly cost-effective for short experiments, iterative development, and workloads with variable duration.
Does AWS offer spot instances for NVIDIA A100 SXM4 80GB?▾
Yes, AWS offers spot/preemptible instances for NVIDIA A100 SXM4 80GB, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.
Does AWS offer reserved instances for NVIDIA A100 SXM4 80GB?▾
Yes, AWS offers reserved instance pricing for NVIDIA A100 SXM4 80GB, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact AWS for current reserved pricing and commitment terms.