Where to rent GPU A100 in the cloud: prices and providers 2026

Renting an NVIDIA A100 GPU in the cloud in 2026 is possible from leading global providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, as well as specialized providers including Lambda Labs, CoreWeave, and Vast.ai, with prices starting from $1.50 to $4.00 per hour for on-demand instances.

The NVIDIA A100 is a flagship accelerator card designed specifically for high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML). Its Ampere architecture, introduced in 2020, still remains one of the most powerful solutions on the market, especially for tasks requiring immense computational power and memory bandwidth. If you are looking to rent a100 gpu, this article will help you navigate the variety of offerings and choose the optimal option.

Why is NVIDIA A100 needed in 2026 and why is it so in demand?

Even several years after its release, the NVIDIA A100 remains a cornerstone in the world of high-performance computing and artificial intelligence. Its unique capabilities make it indispensable for a wide range of tasks, from training complex neural networks to scientific simulations. Understanding these use cases is critically important for those planning to rent an a100.

Training Large Language Models (LLM)

Training large language models, such as GPT-3, GPT-4, LLaMA, and their successors, requires unprecedented computational resources. The A100, with its third-generation Tensor Cores, can perform FP16 and TF32 operations at incredible speeds, significantly accelerating the iterative training process. One of the key features of the A100 is its support for Sparsity — the ability to ignore zero weights in calculations, which doubles performance for sparse models. For LLM training, clusters of tens or hundreds of A100s are often used, connected via NVLink, which provides ultra-fast communication between GPUs. This allows for efficient load distribution and processing of vast amounts of data needed to achieve high accuracy and generative capabilities of the models.

For example, training a model with 70 billion parameters might require several hundred A100 GPUs for weeks or even months. Without a specialized architecture like the A100, these projects would either be impossible or economically unfeasible.

Inference and High-Performance Computing (HPC)

Beyond training, the A100 also excels at inference tasks, i.e., applying already trained models to obtain predictions. In real-time, when millions of users access AI services, inference speed becomes critically important. The A100 is optimized for fast inference execution thanks to its high memory bandwidth (up to 2 TB/s) and support for various data formats, including INT8, which significantly reduces latency and increases throughput. This is especially crucial for applications such as real-time natural language processing, recommendation systems, computer vision, and autonomous driving.

In the HPC domain, the A100 is used for simulating complex physical processes, chemical reactions, climate models, and financial simulations. Its ability to handle FP64 operations with high precision (up to 9.7 TFLOPS) makes it an ideal tool for scientific research where compromises in accuracy are unacceptable. For example, for simulations in materials science or astrophysics, where giant data arrays need to be processed and complex matrix operations performed, the A100 provides the necessary performance.

A100 SXM vs. PCIe: Key Differences for Rental Choice

When choosing where to rent an a100, you will encounter two main form factors: A100 SXM and A100 PCIe. Although both cards use the same Ampere architecture, their physical implementation and connectivity options differ significantly, which directly impacts performance and use cases.

NVIDIA NVLink and Performance

The A100 SXM (Server Module) version is designed specifically for use in high-performance servers, such as NVIDIA DGX systems or specialized cloud instances. The key difference of SXM lies in the use of the NVLink interface, which provides much higher bandwidth for communication between GPUs compared to standard PCIe. Each A100 SXM can have up to 12 NVLink connections, each providing up to 500 Gb/s of bidirectional bandwidth between GPUs. This allows for the creation of powerful multi-GPU clusters (e.g., 8 A100 SXM in a single server) that can exchange data at incredible speeds, virtually eliminating communication bottlenecks. The total NVLink bandwidth in such systems can reach 600 GB/s, which is critically important for large-scale LLM training and other HPC tasks where data must move quickly between GPUs.

The A100 PCIe, on the other hand, connects via a standard PCIe Gen4 x16 slot, which provides up to 64 GB/s of bandwidth. While PCIe Gen4 is significantly faster than previous generations, it is still a bottleneck compared to NVLink, especially in multi-GPU systems. If you plan to use a single A100 or multiple GPUs that do not require intensive data exchange between them, the A100 PCIe may be sufficient. However, for tasks requiring maximum performance in parallel computing across multiple GPUs, such as training very large models, the A100 SXM with NVLink will be preferable.

Compatibility and Availability

The A100 PCIe is more versatile and can be installed in any server with a compatible PCIe Gen4 slot and sufficient power. This makes it more accessible for smaller providers or for setting up your own server. Many cloud providers offer instances with A100 PCIe, which can be more flexible in configuration and sometimes cheaper. For example, you can find dedicated servers with A100 PCIe from some hosts, which can be beneficial for long-term projects that do not require scaling to dozens of GPUs.

The A100 SXM, in contrast, requires a specialized motherboard and cooling system, making it less flexible for self-assembly. It is typically found in pre-built DGX systems or in high-performance instances from large cloud providers that are specifically designed for maximum NVLink performance. This means that if you want to rent an a100 gpu with NVLink, you will likely need to turn to large cloud platforms or specialized HPC providers. The cost of renting A100 SXM instances is usually higher due to their superior performance and infrastructure complexity.

Example: If your project involves fine-tuning a small or medium LLM on a single GPU, the A100 PCIe will be an excellent and more economical choice. However, if you are training a model with hundreds of billions of parameters from scratch and need maximum communication speed between 8 GPUs, then the A100 SXM with NVLink is your only option.

How much does it cost to rent an A100 in the cloud: 2026 price analysis

Prices for renting an a100 in the cloud can vary significantly depending on the provider, region, instance type (SXM or PCIe), and the chosen payment model: on-demand or reserved instance. In 2026, the trend of some price reduction is expected to continue as new generations of GPUs are released, but the A100 will still remain in the premium segment.

On-Demand

On-demand payment is the most flexible option, allowing you to pay only for the actual time used, typically with per-second or per-hour billing. This is an ideal choice for short-term projects, experiments, testing, or tasks with irregular workloads. However, it is also the most expensive option on a per-hour basis.

Approximate on-demand prices for a100 in the cloud price in 2026:

• AWS (Amazon Web Services):
• P4d.24xlarge (8x A100 40GB SXM): from $32.77 per hour (i.e., about $4.10 per GPU per hour).
• P4de.24xlarge (8x A100 80GB SXM): from $40.96 per hour (i.e., about $5.12 per GPU per hour).
• Google Cloud Platform (GCP):
• A2-highgpu-8g (8x A100 40GB SXM): from $35.00 per hour (i.e., about $4.37 per GPU per hour).
• A3-highgpu-8g (8x A100 80GB SXM): from $44.00 per hour (i.e., about $5.50 per GPU per hour).
• Microsoft Azure:
• Standard_ND96asr_v4 (8x A100 40GB SXM): from $39.60 per hour (i.e., about $4.95 per GPU per hour).
• Specialized providers (Lambda Labs, CoreWeave, Vast.ai):
• A100 40GB PCIe: from $1.50 to $2.50 per hour.
• A100 80GB PCIe: from $2.00 to $3.50 per hour.
• A100 80GB SXM (in clusters): from $3.00 to $4.50 per hour.

It is important to note that specialized providers often have lower prices, especially for individual GPUs, but may lack some enterprise features and SLAs of larger clouds.

Reserved Instances

Reserved Instances (RI) are an option where you commit to using a resource for a specified period (typically 1 or 3 years) in exchange for a significant discount compared to on-demand prices. Discounts can reach 50-70% or more, making RIs extremely cost-effective for long-term, predictable projects. This is an ideal choice for companies that constantly train or use large models.

Approximate discounts and prices for renting an a100 gpu via RI (for 1 year usage, no upfront payment):

• AWS: Discounts up to 40-50% off on-demand prices. The cost of an A100 40GB can drop to $2.00 - $2.50 per GPU per hour.
• GCP: Commitments for 1 or 3 years also provide significant discounts. An A100 40GB can cost around $2.20 - $2.70 per GPU per hour.
• Azure: Discounts up to 40-50% when reserving for 1 or 3 years. An A100 40GB can drop to $2.40 - $2.90 per GPU per hour.

For 3-year RIs with full upfront payment, discounts can be even greater. When choosing RIs, it is necessary to carefully plan your needs, as you commit to paying for the resource regardless of its actual usage. Before deciding on an RI, it is recommended to conduct a pilot project on on-demand instances to accurately assess your needs.

Where to Rent A100 GPU: Comparison of Leading Providers

The choice of provider for renting an a100 depends on many factors: budget, project scale, infrastructure requirements, and personal preferences. Let's consider the key players in the market.

AWS EC2 P4d/P5

Amazon Web Services (AWS) offers some of the most powerful A100 instances through its EC2 service. The P4d and P5 series (with NVIDIA H100, but P4d is still relevant for A100) provide access to A100 SXM with NVLink. P4d.24xlarge instances are equipped with 8 A100 40GB GPUs, and P4de.24xlarge with 8 A100 80GB GPUs.

• Advantages: Deep integration with the extensive AWS ecosystem (S3, SageMaker, EKS), high availability, global coverage, Enterprise-level support. Ideal for large companies with existing AWS infrastructure.
• Disadvantages: Complex pricing policy, can be more expensive for small projects, requires deep AWS knowledge for optimization.
• Typical scenarios: Large-scale LLM training, HPC, deployment of complex ML pipelines.

Example of launching an A100 instance on AWS:

aws ec2 run-instances \
    --image-id ami-xxxxxxxxxxxxxxxxx \
    --instance-type p4d.24xlarge \
    --key-name my-key-pair \
    --security-group-ids sg-xxxxxxxxxxxxxxxxx \
    --subnet-id subnet-xxxxxxxxxxxxxxxxx \
    --count 1

For more detailed cost planning and selection of optimal solutions, especially in the context of high-performance tasks, it may be useful to familiarize yourself with materials on Oracle Cloud alternatives, as many selection and migration principles apply to other cloud providers as well.

Google Cloud A2/A3

Google Cloud Platform (GCP) is also a leader in providing GPU resources. A2 (with A100 40GB) and A3 (with A100 80GB) instances offer powerful configurations, including up to 16 A100 GPUs in a single A3 instance, connected by NVLink and a specialized IPU (Infrastructure Processing Unit) interconnect.

• Advantages: Excellent performance, especially with A3 instances, strong integration with Google AI tools (Vertex AI, TensorFlow), competitive prices for reserved resources.
• Disadvantages: May be less familiar to users unfamiliar with the Google ecosystem.
• Typical scenarios: LLM training and inference, scientific research, projects using TensorFlow and JAX.

Example of creating a VM with A100 on GCP:

gcloud compute instances create my-a100-vm \
    --zone=us-central1-a \
    --machine-type=a2-highgpu-8g \
    --accelerator=type=nvidia-a100-40gb,count=8 \
    --image-project=debian-cloud \
    --image-family=debian-11 \
    --boot-disk-size=200GB

Microsoft Azure ND A100 v4

Microsoft Azure offers the ND A100 v4 series, which also uses A100 SXM. These instances are optimized for large-scale AI and HPC tasks, offering up to 8 A100 40GB or 80GB GPUs in a single node, connected by NVLink.

• Advantages: Strong integration with Microsoft products (Azure ML), support for HPC scenarios, attractive offers for enterprise customers.
• Disadvantages: May be more expensive for small projects, requires familiarity with Azure.
• Typical scenarios: Enterprise ML projects, HPC simulations, deep learning.

Other Providers (Lambda Labs, CoreWeave, OVHcloud, Vast.ai)

Beyond the "big three," there are a number of specialized providers who can offer more favorable terms, especially for individual GPUs or less extensive projects. They often focus on GPU computing and can be more flexible in pricing.

• Lambda Labs: Known for their specialization in GPU clouds, offering A100 40GB and 80GB (PCIe and SXM) at competitive prices, often lower than major clouds. Simple interface, good support for ML developers.
• CoreWeave: Offer a wide range of GPUs, including A100, with flexible pricing models. Focused on ML and visualization, with excellent network infrastructure.
• OVHcloud: A European provider offering dedicated servers with A100 PCIe. Can be a good choice for projects with data localization requirements in Europe.
• Vast.ai: A decentralized platform that allows renting GPUs from private owners. Prices can be significantly lower than market rates, but availability and stability depend on the specific host. An excellent option for budget experiments or short-term tasks where stability compromises are acceptable.

Table: Comparison of A100 Providers (Approx. On-Demand Prices per 1x A100 80GB per hour, 2026)

When a Cheaper Card is Enough: A100 Alternatives

Despite the outstanding performance of the A100, not every project requires its full power. Often, more affordable GPUs can suffice, significantly reducing the cost of renting an a100. The right card choice depends on specific tasks, budget, and performance requirements.

NVIDIA H100, L40S, A6000, RTX 4090

There are many other powerful GPUs on the market that may be more suitable for certain scenarios:

• NVIDIA H100: The successor to the A100, based on the Hopper architecture. Offers a significant performance increase (up to 3-6x for some tasks) compared to the A100, especially for LLM training. However, the H100 is significantly more expensive and less available. Ideal for the most advanced and resource-intensive projects where the A100 is no longer sufficient.
• NVIDIA L40S: A professional card based on the Ada Lovelace architecture, focused on inference, 3D visualization, and some ML tasks. It has a large memory capacity (48GB) and good FP32 performance, but it lags behind the A100 in tensor operations and FP64. It can be a good alternative for inferencing large models or for tasks requiring a lot of VRAM but not extreme training speed.
• NVIDIA RTX A6000: A professional card based on the Ampere architecture (like the A100), but with an emphasis on workstations and professional applications. It has 48GB of GDDR6 memory. An excellent choice for computer vision, CAD/CAM, rendering, and some ML tasks where FP64 is not critical, but memory capacity is important. Rental price is usually lower than for the A100.
• NVIDIA RTX 4090: A consumer card based on the Ada Lovelace architecture. It boasts outstanding FP32 performance and 24GB of GDDR6X memory. Due to its lower rental cost and high FP32 performance, the RTX 4090 is an incredibly attractive option for experiments, fine-tuning small LLMs, development, and gaming servers. For some ML tasks, it can compete with the A100, especially if your code is well-optimized for Ada Lovelace.

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Assessing Project Needs

To determine whether you need an A100 or a cheaper card, ask yourself the following questions:

1. Data volume and type: How large is your model? How much training data? For models with billions of parameters and huge datasets, A100 or H100 are almost indispensable. For models with millions of parameters or for fine-tuning, RTX 4090 or A6000 may be sufficient.
2. Training/inference speed requirements: Do you need the fastest training speed? Are there strict real-time inference latency requirements? If so, A100 (or H100) with NVLink will be the best choice. If speed is not critical, slower but cheaper options can be considered.
3. Computational precision (FP64): Do you need high-precision FP64 computations (e.g., for scientific simulations)? The A100 offers excellent FP64 performance. Most consumer GPUs have limited FP64 support.
4. Budget: How flexible is your budget? The difference in rental price between an A100 and, for example, an RTX 4090 can be several times.
5. Availability: Which cards are available from your preferred provider and in your region?

For small projects where a single GPU is sufficient, the RTX 4090 is often the "sweet spot," offering phenomenal performance for its price. For inference, the L40S or A6000 might be more economically viable if the A100 is overkill. Always start by assessing the minimum necessary resources and scale them as your project grows.

How to Order and Configure an A100 in the Cloud: Step-by-Step Guide

The process of ordering and configuring an A100 in the cloud, whether from a large provider or a specialized service, involves common steps. It is important to follow them to maximize resource utilization and avoid unnecessary costs.

Choosing an Instance and OS

1. Registration and provider selection: Register with your chosen provider (AWS, GCP, Azure, Lambda Labs, etc.). Ensure your account has sufficient quotas to launch GPU instances, as A100 often requires a separate request for a quota increase. 2. Region selection: Choose a region that is geographically closer to you or your target audience to minimize latency. Also consider A100 availability in different regions. 3. Instance type selection: Decide whether you need A100 SXM (for maximum performance and NVLink) or A100 PCIe (more versatile). Select the appropriate instance (e.g., `p4d.24xlarge` on AWS, `a2-highgpu-8g` on GCP). 4. Operating system selection: For GPU computing, Linux distributions are almost always chosen, most often Ubuntu or CentOS. Many providers offer pre-built images (AMI, VM Image) with pre-installed NVIDIA drivers, CUDA, and even popular frameworks (TensorFlow, PyTorch). This significantly simplifies initial setup. If no pre-built image is available, choose a clean Ubuntu Server LTS.

Installing Drivers and CUDA

If you have chosen a clean OS image, you will need to manually install NVIDIA drivers and the CUDA Toolkit. This process is critically important for the correct operation of the GPU.

1. Connect to the instance: Use SSH to connect to your cloud instance.

ssh -i /path/to/your/key.pem ubuntu@your-instance-ip

2. Update the system:

   sudo apt update && sudo apt upgrade -y

3. Install NVIDIA drivers:

   For Ubuntu, this can be done via PPA:

   sudo add-apt-repository ppa:graphics-drivers/ppa -y
   sudo apt update
   sudo apt install nvidia-driver-535 -y # Or the current stable version
   sudo reboot

   After rebooting, verify the installation:

   nvidia-smi

   You should see information about your A100 GPU.

4. Install CUDA Toolkit:

   Download the CUDA Toolkit from the official NVIDIA website, selecting your OS and version. Use `wget` on the server.

   wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
   sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
   wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda-repo-ubuntu2204-12-2-2-local_12.2.2-1_amd64.deb # Replace with the current version
   sudo dpkg -i cuda-repo-ubuntu2204-12-2-2-local_12.2.2-1_amd64.deb
   sudo cp /var/cuda-repo-ubuntu2204-12-2-2-local/cuda-*-keyring.gpg /usr/share/keyrings/
   sudo apt update
   sudo apt -y install cuda-toolkit-12-2 # Replace with the current version

   Add CUDA to environment variables:

   echo 'export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}' >> ~/.bashrc
   echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc
   source ~/.bashrc

   Verify CUDA installation:

   nvcc --version

5. Install libraries and frameworks: Install cuDNN, then Python, pip, and your ML frameworks (PyTorch, TensorFlow) with CUDA support.

Cost Optimization and Monitoring

1. Usage monitoring: Use `nvidia-smi` and cloud monitoring tools (CloudWatch, Stackdriver) to track GPU load, memory, and temperature. This will help ensure you are effectively utilizing the rented resource. 2. Automated shutdown: Set up scripts or cloud functions that will automatically shut down the instance when it is not in use. This will save significant costs on on-demand rates. 3. Spot Instances: If your project is tolerant to interruptions, consider using Spot Instances (AWS) or Preemptible VMs (GCP). They are significantly cheaper than on-demand but can be revoked by the provider. Excellent for training models that can save checkpoints. 4. Reserved Instances: For long-term and stable workloads, consider purchasing Reserved Instances or Commitments to get significant discounts. 5. Code optimization: Ensure your code maximizes GPU capabilities. Profiling and optimization can significantly reduce task execution time and, consequently, rental costs. Use NVIDIA Nsight Systems and Nsight Compute tools.

Conclusion

Renting an NVIDIA A100 in the cloud in 2026 remains a key solution for the most demanding tasks in AI, ML, and HPC. The choice of provider and instance type should be based on a thorough analysis of project needs, budget, and performance requirements, as well as considering the difference between A100 SXM and PCIe. For smaller projects or experiments, more economical alternatives such as the RTX 4090 or A6000 should be considered, while for large-scale and critical tasks, the A100 (or H100) from leading cloud providers will be the optimal choice.