A100 on Azure: Pricing, Specs & How to Rent

Contents

• A100 GPU Specifications
• Azure A100 Pricing
• Renting A100 on Azure
• Competitive Analysis
• Use Cases & Performance
• FAQ
• Related Resources
• Sources

A100 GPU Specifications

A100 Azure Pricing is the focus of this guide. The NVIDIA A100 is a high-capacity data center GPU. 80GB of HBM2e memory, both PCIe and SXM variants. 6,912 CUDA cores deliver parallelism for training and inference.

Key specs:

• Memory: 80GB HBM2e
• Memory Bandwidth: 2.0 TB/s (SXM) or 1.935 TB/s (PCIe)
• CUDA Cores: 6,912
• Peak FP32 Performance: 19.5 TFLOPS
• Peak Tensor Performance: 312 TFLOPS (BF16/TF32)
• Note: FP8 is not natively supported on the A100 (introduced in Hopper/H100)

Good for batch inference, distributed training, analytics. High memory capacity handles large models and multi-model serving.

Learn more about GPU specifications in the A100 specs guide.

Azure A100 Pricing

Azure offers A100 access through its Standard and Premium VM tiers. As of March 2026, pricing depends on region, reserved capacity, and machine size.

Standard on-demand rates start at approximately $3.67 per hour for a single A100 80GB instance. Reserved instances provide 30-50% discounts for annual commitments.

Pricing components:

• Compute (per hour): ~$3.67 (single A100 80GB)
• Storage (per GB/month): $0.12-0.20
• Egress (per GB): $0.02-0.12
• Reserved Discount: 30-50% off on annual plans

Azure clusters can scale to multiple A100s simultaneously. Multi-GPU pricing applies per unit, so an 8-GPU cluster runs roughly 8x the single-GPU rate.

For broader price comparison, review RunPod GPU pricing and Google Cloud GPU pricing.

Renting A100 on Azure

Azure A100 provisioning follows these steps:

1. Sign into Azure Portal
2. Create a new Virtual Machine
3. Select compute image (Ubuntu 22.04 recommended)
4. Choose an A100 SKU — the primary options are the ND A100 v4 series (Standard_ND96asr_v4, 8xA100 80GB SXM) for distributed training, or the NC A100 v4 series (Standard_NC24ads_A100_v4, 1xA100 80GB PCIe) for single-GPU workloads
5. Configure vCPU count and memory
6. Select preferred region and availability zone
7. Configure storage and networking
8. Review and deploy

Azure provides pre-configured machine images with CUDA toolkit, cuDNN, and PyTorch pre-installed. Deployment typically completes in 5-10 minutes.

SSH access enables immediate connection. Users can clone repositories, install dependencies, and launch workloads directly.

Popular deployment patterns include:

• Single-node batch training
• Multi-node distributed training (with InfiniBand)
• Real-time inference serving
• Batch processing pipelines

For alternative providers, explore Azure GPU pricing for full details on Azure's broader GPU ecosystem.

Competitive Analysis

Multiple cloud providers offer A100 access. Selection depends on pricing, regions, and service integrations.

Azure excels for large teams: Active Directory, DevOps integration, compliance certifications. Smaller teams prefer RunPod or Lambda for lower costs.

Use Cases & Performance

A100 performance suits diverse ML scenarios. For training, throughput reaches 500-700 tokens/second on 7B-parameter models in mixed-precision mode.

Inference benchmarks:

• Llama 2 7B: 600-800 tokens/sec
• Llama 2 13B: 400-500 tokens/sec
• Llama 2 70B: 80-120 tokens/sec

Fine-tuning performance improves with parameter-efficient techniques like LoRA, reducing memory overhead by 60%.

Common applications:

• Training custom language models
• Fine-tuning on proprietary datasets
• Multi-model inference serving
• Large-scale batch processing
• Computer vision training tasks

FAQ

What is the minimum rental period on Azure? Azure charges per minute, so users can rent for as little as an hour.

Can I resize an A100 instance after launch? Only when the instance is stopped. Resizing requires restarting, which interrupts workloads.

Does Azure provide spot A100 pricing? Yes, spot instances cost 30-50% less but risk interruption.

Is data transfer included in the hourly rate? No. Egress charges apply separately at $0.02-0.12 per GB.

What frameworks does Azure support? PyTorch, TensorFlow, JAX, and other CUDA-compatible libraries work directly.

Related Resources

• GPU Pricing Guide
• Inference Optimization
• Fine-Tuning Guide
• AWS GPU Pricing
• LLM API Pricing

Sources

• NVIDIA A100 Tensor GPU Product Brief
• Microsoft Azure Virtual Machines Pricing (official pricing page)
• NVIDIA CUDA Compatibility Matrix