A100 SXM4 40GB on AWS
Visit AWSAWS delivers the NVIDIA A100 SXM4 40GB GPU through high-performance instances like p4d.24xlarge (8x A100s) and p4de.24xlarge, optimized for demanding AI, ML, and HPC workloads. This combination stands out for its seamless integration within AWS's vast ecosystem, enabling scalable training of large language models and deep learning tasks with up to 40GB HBM2e VRAM per GPU. Built on Ampere architecture, the A100 offers 19.5 TFLOPS FP64, 156 TFLOPS FP16 (with sparsity), and Multi-Instance GPU (MIG) for partitioning. Target audience includes large enterprises and research teams needing global redundancy across 30+ regions, deep service integration (e.g., SageMaker for managed ML pipelines), and cost efficiency via per-second billing and Spot Instances. Key value propositions: Elastic Fabric Adapter (EFA) for low-latency multi-node scaling, high-bandwidth NVLink intra-instance interconnects, and compatibility with AWS storage like FSx Lustre for petabyte-scale datasets. While premium pricing reflects top-tier performance, it's ideal for production-grade deployments requiring reliability and orchestration tools.
Why NVIDIA A100 SXM4 40GB on AWS?
Choose AWS for NVIDIA A100 SXM4 40GB when enterprise-grade infrastructure and ecosystem lock-in are priorities. AWS's global network of Availability Zones ensures high availability and low-latency data transfer worldwide. Unique advantages include deep SageMaker integration for end-to-end ML workflows, from notebooks to distributed training, and hybrid options with Trainium/Inferentia for cost-optimized inference post-training. Per-second billing with Spot Instances (up to 90% savings) complements the GPU's power efficiency, ideal for variable workloads. p4d/p4de instances provide 400 Gbps EFA networking and NVLink 3.0 (600 GB/s bidirectional per GPU pair), maximizing A100's scalability for multi-node jobs. Robust security (e.g., Nitro Enclaves) and managed services reduce ops overhead, making this combo superior for teams prioritizing integration over raw cost.
Live Pricing
Real-time NVIDIA A100 SXM4 40GB offers from AWS
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 AWS | 8×NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 96 vCPU 1152GB RAM | Virginia | $4.10/GPU/hr $32.77/hr total (8×) | |||
AWS | 8×NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 96 vCPU 1152GB RAM | Oregon | $4.10/GPU/hr $32.77/hr total (8×) |
Performance Notes
On AWS p4d.24xlarge, expect peak A100 SXM4 40GB performance: 312 TFLOPS FP16 aggregate (8 GPUs), with NVLink enabling efficient multi-GPU communication at 600 GB/s per link. Inter-instance scaling leverages EFA (up to 400 Gbps), supporting frameworks like PyTorch DDP for large-model training (e.g., GPT-scale). Storage via EBS (19.2 TB NVMe local SSD) or FSx Lustre (exabyte-scale, 100s GB/s throughput) handles massive datasets. Benchmarks show near-native NVIDIA perf, but real-world varies by workload—strong for Transformer training, slightly throttled in memory-bound tasks vs. on-prem. Multi-node jobs scale linearly to thousands of GPUs via Slurm/P2P. Unknowns: exact current utilization/queue times; test with AWS benchmarks for your stack.
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
40GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA A100 SXM4 40GB on AWS is straightforward via EC2 p4d/p4de instances. Use Deep Learning AMIs preloaded with CUDA 11.x, cuDNN, and frameworks like TensorFlow/PyTorch. Ideal for quick prototyping or production training; leverage Spot for savings and SageMaker for managed scaling.
Steps
- 1Sign in to AWS Management Console and ensure IAM permissions for EC2/SageMaker.
- 2Navigate to EC2 Dashboard > Launch Instance; select 'Deep Learning AMI GPU PyTorch' or similar.
- 3Choose p4d.24xlarge or p4de.24xlarge; configure vCPU/RAM, add EBS/FSx storage.
- 4Set up security groups (SSH/HTTP), launch with Spot if cost-sensitive, then connect via SSM/SSH.
- 5Install dependencies (e.g., NCCL for multi-GPU) and run nvidia-smi to verify.
Pro Tips
- Bid aggressively on Spot Instances for p4d (often 70-90% off) to cut costs on interruptible training jobs.
- Use SageMaker Distributed Training for automatic multi-node orchestration instead of manual Slurm setup.
- Pair with FSx Lustre for high-throughput data loading in large-scale pre-training workflows.
Frequently Asked Questions
What is AWS's billing model for NVIDIA A100 SXM4 40GB?▾
AWS bills per-second for GPU instances including NVIDIA A100 SXM4 40GB. 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 40GB?▾
Yes, AWS offers spot/preemptible instances for NVIDIA A100 SXM4 40GB, 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 40GB?▾
Yes, AWS offers reserved instance pricing for NVIDIA A100 SXM4 40GB, 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.
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