10 Best GPU Cloud Providers in 2026

By 2034, the GPU Cloud Provider Service Market is projected to reach USD 90.7 billion, expanding at a 32% CAGR. With demand rising this quickly, choosing the right cloud GPU provider is more critical than ever. The answer upfront: the best providers offer modern cloud based GPUs, transparent pricing and high-speed networking for AI and ML workloads. This guide offers a data-driven comparison, enabling you to identify the right platform quickly.

We explore top cloud GPU providers, including Hyperstack, Runpod, Lambda Labs, Nebius, Paperspace and more.

Top 10 Cloud GPU Providers List

A GPU as a service provider gives on-demand access to GPUs (H100, A100, L40s) for AI and ML training, fine-tuning, and inference. We compared 10 GPU as a service companies on pricing, NVLink/RDMA support, network bandwidth and use cases, highlighting when to choose.

What are the Best Cloud GPU Providers

Here is a list of affordable cloud GPU providers for AI for 2025:

1. Hyperstack

Hyperstack by NexGen Cloud offers a real cloud environment where you can build with AI and deploy market-ready products faster. With instant access to GPU for AI like the NVIDIA H100, A100, L40 and RTX A6000/A40 and a developer-friendly dashboard, Hyperstack is designed to support every stage of your AI and ML workflow.

Hyperstack delivers enterprise-grade cloud GPUs like NVIDIA H100, A100, and L40 with NVLink, high-speed 350Gbps networking, and VM hibernation for cost savings. Our AI Studio supports Gen AI workflows, LLM fine-tuning, and inference. Ideal for scalable AI, HPC and rendering workloads with pay-as-you-go flexibility.

Key Features

Hyperstack empowers AI innovators with lightning-fast GPUs and developer-first tooling:

• NVLink support for A100 and H100 GPUs for scalable training and inference

• High-speed networking up to 350Gbps for low-latency and high-throughput workloads

• VM Hibernation to pause unused workloads and control costs

• 1-click deployment for fast and easy project setup

• NVMe block storage for enhanced data access and performance

• Green infrastructure, powered by 100% renewable energy

• AI Studio: End-to-end Gen AI platform for LLM fine-tuning, evaluation and deployment

Cloud GPU Pricing

• Pay-as-you-go with minute-by-minute billing 

• Reservation options available for long-term savings

• Spot VMs with 20% lower pricing than standard pricing

Ideal Use Cases

• AI Experimentation 

• AI training, fine-tuning & inference

• Machine learning at scale

• Generative AI

• Large language models (LLMs) 

• High-performance computing (HPC) 

• Rendering

2. Runpod

Runpod offers fast, serverless GPU compute with container-based environments and real-time analytics. Developers can deploy AI workloads instantly without managing infrastructure. It supports A4000, A100, and MI300X GPUs, making it perfect for real-time model iteration, LLM deployments, and flexible MLOps pipelines.

Key Features

• Serverless GPU compute

• Real-time analytics and logs

• Support for custom containers and volume mounting

Pricing

• A4000: $0.17/hour

• A100 PCIe: $1.19/hour

• MI300X: $3.49/hour

Ideal Use Cases

Runpod is ideal for real-time model iteration, containerised AI workflows, and serverless LLM deployments.

3. Lambda Labs

Lambda Labs provides deep-learning-optimised infrastructure with NVIDIA H100 and H200 GPUs. Featuring Quantum-2 InfiniBand and preinstalled Lambda Stack, it simplifies LLM training, inference, and scalable cluster setups for enterprises. Ideal for teams needing high-performance, preconfigured environments for AI research and production.

Key Features

• Lambda Stack with preinstalled ML libraries

• One-click GPU cluster setup

• Quantum-2 InfiniBand networking for low latency

Pricing

• H100 PCIe: $2.49/hour

Ideal Use Cases

Lambda Labs is ideal for LLM training, enterprise-grade inference, and teams seeking scalable, preconfigured AI environments.

4. Thunder Compute

If you’re looking for powerful GPU cloud server without the hefty price tag of the big cloud players, Thunder Compute is worth checking out. With one-click deployment, flexible scaling and prices up to 80% lower, it’s built for developers who want to move fast and stay focused on their work.

Key Features

• Wide choice of GPUs, including Tesla T4, A100 40 GB and A100 80 GB

• Flexible vCPU, RAM, and disk options to match your workload

• High-speed networking with 7–10 Gbps throughput

• Zero-downtime scaling and easy GPU swaps

• Seamless VS Code integration with no SSH keys or CUDA hassles

• Pre-built templates for popular ML and LLM frameworks like Ollama and Comfy-UI

Pricing

Thunder Compute’s pricing is transparent. For on-demand GPUs, their pricing is:

• Tesla T4 at $0.27/hr

• A100 40 GB at $0.66/hr

• A100 80 GB at $0.78/hr

Ideal Use Cases

Thunder Compute is a great fit for:

• Data scientists running experiments and model training at scale

• ML researchers exploring new architectures and fine-tuning techniques

• AI-first startups needing enterprise-grade GPUs without breaking the budget

• Students and learners looking for affordable, reliable GPU rentals for projects and coursework

• Teams running inference who want low-latency, production-ready infrastructure without heavy ops overhead

5. Paperspace (DigitalOcean)

Paperspace delivers scalable GPU cloud infrastructure with fast-start templates and version control, making it ideal for dev teams building and deploying AI applications. 

Paperspace combines simplicity with scalability, offering NVIDIA H100 and A100 GPUs for AI development. It supports fast-start templates, version control, and MLOps integration. Ideal for experimentation, training, and deploying AI applications efficiently within collaborative environments.

Key Features

• Pre-configured templates

• Auto versioning and experiment reproducibility

• Flexible scaling and multi-GPU support

Pricing

• H100: $2.24/hour

• A100: $1.15/hour

Ideal Use Cases

Paperspace is ideal for model development, MLOps pipelines, experimentation, and scalable model deployment.

6. Nebius

Nebius delivers InfiniBand-powered GPU compute with APIs, Terraform, and elastic scaling. With NVIDIA H100 and A100 options, it supports high-speed distributed AI training. Ideal for developers and enterprises needing automated, scalable infrastructure for advanced ML and inference workloads.

Key Features

• API, Terraform, and CLI access

• Elastic scaling with custom configurations

• InfiniBand-enabled networking

Pricing

• H100: From $2.00/hour

Ideal Use Cases

Nebius is ideal for scalable AI/ML workloads, inference at scale, and multi-node distributed training.

7. Vast.ai

Vast.ai operates a decentralised GPU marketplace offering the lowest-cost compute via real-time bidding. Developers can instantly deploy Docker-based environments across varied GPUs. Ideal for cost-sensitive AI training, experimentation, and projects demanding flexible, budget-friendly GPU resources.

Key Features

• Auction-based GPU pricing

• Instant deployment via Docker

• Simple web interface and CLI

Pricing

• Variable, based on bidding

Ideal Use Cases

Vast.ai is ideal for low-cost model training, experiment-heavy projects, and developers needing flexibility in budget.

8. Genesis Cloud

Genesis Cloud provides EU-sovereign, sustainable GPU infrastructure featuring HGX H100 and GB200 NVL72 clusters. Tailored for Gen AI and LLM workloads, it ensures data compliance and scalability. Ideal for European enterprises and research teams training large models at scale.

Key Features

• EU-compliant, sovereign cloud infrastructure

• GB200 NVL72 support for next-gen AI compute

• Efficient multi-node training at scale

Pricing

• HGX H100: $2.00/hour

Ideal Use Cases

Genesis Cloud is ideal for training LLMs and running Gen AI platforms.

9. Vultr

Vultr offers global GPU availability with 32 data centers supporting NVIDIA H100, A100, and L40 GPUs. Its on-demand and reserved instances enable distributed AI training and inference worldwide. Ideal for edge deployments, multi-region model scaling, and deep learning workloads.

Key Features

• Broad data centre coverage

• On-demand and reserved GPU instances

• Competitive GPU pricing

Pricing

• L40: $1.671/hour

• H100: $2.30/hour

Ideal Use Cases

Vultr is ideal for distributed deep learning, inference at edge locations, and multi-region model deployment.

10. Gcore

Gcore combines GPU compute with a strong CDN and enterprise-grade security. Offering global reach, DDoS protection, and custom infrastructure planning, it’s suited for regulated industries running secure, distributed AI pipelines and edge inference workloads.

Key Features

• 180+ CDN locations

• Enterprise-level DDoS protection

• Infrastructure planning for custom AI needs

Pricing

• Custom

Ideal Use Cases

Gcore is ideal for enterprise AI, edge inference with CDN integration, and secure distributed AI pipelines.

Cloud GPU Guide: Pricing, Availability and Deployment Models

Check out the factors to consider before cloud GPU rental:

a) Cheapest Cloud GPU 

If you’re looking for an affordable entry point,  Hyperstack’s RTX A6000 at $0.50/hour could be an ideal choice. Hyperstack also offer a reservation option and Spot VMs at a lower price.

b) H100 SXM vs PCIe Availability

Most providers now offer both H100 PCIe and SXM variants. Hyperstack offers NVIDIA H100 PCIe at $1.90/hour and NVIDIA SXM at $2.40/hour. These GPU VMs on Hyperstack also support high-speed networking of up to 350Gbps.

c) Serverless GPU Clouds

Runpod and Hyperstack are strong options for serverless GPU compute. Runpod focuses on container-based, serverless GPU sessions with auto-scaling. Hyperstack’s AI Studio provides a serverless, no-code/low-code layer for fine-tuning and inference without provisioning infrastructure manually.

d) Bare-Metal GPU Providers

For direct access to bare-metal performance, Lambda Labs, Genesis Cloud and OVHcloud provide dedicated GPU nodes without virtualisation overhead. These are best suited for HPC, LLM training at scale, and enterprise deployments requiring maximum control.

Conclusion

Choosing the right cloud GPU server providers depends on your needs, budget, and performance requirements. Each cloud provider offers distinct advantages, whether cost-effective solutions for small-scale projects or powerful GPUs designed for AI and ML workloads. Our balanced approach to providing advanced GPUs with high-performing features ensures you deploy your workloads at their level best.

Get started today and enjoy all the benefits Hyperstack has to offer.

See our Quick Start demo video below to get started!

FAQs

What is a GPU cloud provider?

A GPU cloud service offers access to high-performance GPUs located in the cloud. These processors are engineered to manage complex graphical and parallel processing tasks, including rendering, AI, and machine learning workloads.

Can GPU cloud services be used for large language models (LLMs)?

Yes, GPU cloud services are well-suited for training and deploying large language models. Providers like Hyperstack offer high-performance GPUs such as the NVIDIA A100, NVIDIA H100 SXM and NVIDIA H100 PCIe, which are ideal for handling the compute and memory demands of LLMs. For more advanced large models, multi-GPU setups or distributed computing support is essential to ensure scalability and performance. On Hyperstack, you can choose NVIDIA H100 with NVLink and NVIDIA A100 with NVLink for seamless scaling.

Which is the best cloud GPU provider for AI?

The best cloud GPU provider for AI depends on your specific workload, budget, and location requirements. Providers like Hyperstack, Lambda Labs, and Runpod offer access to high-performance GPUs such as the NVIDIA A100 and H100 series, which are widely used for training and deploying AI models.

Which cloud providers offer dedicated GPU-powered virtual machines?

Several cloud platforms offer dedicated GPU-powered virtual machines for tasks like AI training, deep learning and inference. Popular options include Hyperstack, Lambda Labs, Vultr, and Runpod, each offering different GPU models and configurations.

Where can I rent cloud GPUs for complex computations?

Cloud GPUs for demanding tasks such as large-scale training, scientific computing, or data analysis can be rented from platforms like Hyperstack, Vast.ai or Genesis Cloud, which provide access to a variety of GPU types at different performance and price points.

How secure are cloud GPU services?

Most reputable affordable cloud gpu provider implement industry-standard security measures such as data encryption, access controls, and compliance with certifications like ISO 27001 or SOC 2 to ensure the protection of user data and workloads.

Which is the best cloud GPU provider for deep learning?

Deep learning workloads benefit from providers that offer a range of GPU models, fast storage, and networking options. Platforms such as Hyperstack are commonly used depending on workload needs.

What is the price of a Cloud GPU?

The cost of a cloud GPU can differ greatly based on the GPU model, the cloud provider and the instance setup, but typically begins at $0.95 per hour for the NVIDIA A100 GPU on Hyperstack.

Which is the best cloud GPU for LLMs?

GPUs like the NVIDIA A100 and NVIDIA H100 are widely regarded as the most suitable for LLM workloads due to their high memory bandwidth, tensor performance, and scalability. The right choice depends on model size, training duration, and parallelisation needs.

Which is the best cloud GPU provider for small businesses?

Hyperstack is the best as it offers affordable GPUs with high-performance, starting for NVIDIA RTX A6000 at $0.50/hour.

Which providers offer H100 SXM vs PCIe?

Hyperstack, Paperspace, Vultr, and Genesis Cloud list both PCIe and SXM variants. SXM models typically cost more but enable NVLink, making them better for multi-GPU setups.

What are serverless GPU cloud providers?

Runpod and Hyperstack offer serverless GPU options, enabling developers to run workloads without managing the underlying infrastructure.

Who offers bare-metal GPU servers?

Lambda Labs, Genesis Cloud, and OVHcloud provide bare-metal GPU instances, giving users full hardware access for maximum performance.

What cloud services provide on-demand GPU VM?

Hyperstack provides on-demand GPU virtual machines with instant access to high-performance GPUs like NVIDIA H100, NVIDIA A100, NVIDIA L40 and NVIDIA RTX A6000. You can spin up VMs quickly for AI training, inference, fine-tuning, and HPC workloads, with developer-friendly dashboards and 1-click deployment.

Which cloud GPU provider has the most flexible pricing options?

Hyperstack offers the most flexible pricing options with pay-as-you-go billing, minute-by-minute charges, reservation options for long-term savings, and Spot VMs at 20% lower pricing than standard rates. This enables users to optimise costs based on workload needs.