Top 5 Cloud GPU Rental Platforms Compared: Pricing, Performance and Features

Let’s be real, training large AI models or fine-tuning LLMs on consumer-grade GPUs is painful. You’re waiting hours (sometimes days), your machine’s on fire and worst of all? You’re still not even close to deployment. You may also be trying cloud services, only to realise they are burning through your budget faster than your model is overfitting.

Sound familiar?

That’s exactly why cloud GPU rental platforms are everyone's go-to choice now. You just need to rent a cloud GPU according to your workload and pay for what you use. But with so many options in the market, how do you pick the one that’s actually worth your money?

Top 5 Cloud GPU Rental Platforms

Our latest guide compares the top 5 cloud GPU rental platforms so you don’t have to. By the end of it, you’ll know exactly where to run your AI workloads, whether you're fine-tuning, training or deploying for inference.

1. Hyperstack 

Hyperstack is a high-performance cloud GPU platform built for modern AI/ML workloads like training, fine-tuning and real-time inference at scale. Unlike most platforms that offer generic virtual machines with shared resources, Hyperstack gives you dedicated GPU infrastructure in a real cloud environment that is fast, reliable and enterprise-grade.

Key Features of Hyperstack

The key features of the Hyperstack cloud GPU platform include:

• Enterprise-grade GPUs: When you need serious GPU power, you can rent enterprise-grade NVIDIA GPUs like the NVIDIA H100 SXM with NVSwitch and NVLink for heavy-duty training, fine-tuning and large-scale inference. In case your workload is not demanding, you can switch to NVIDIA RTX A6000 or L40 for a budget.
• High-speed networking: Speed matters the most in AI workloads. You’ve got it. With up to 350 Gbps networking, you can move massive datasets fast and run distributed workloads with minimal latency on Hyperstack, so no waiting around for slow transfers.
• 1-click deployment: You don’t have to waste time setting things up. When you're ready to build, just click and deploy your GPU VM in minutes and start straight into training, testing or iterating.
• On-demand Kubernetes: Easily spin up Kubernetes clusters for containerised AI workloads, no YAML files or cluster management needed. Hyperstack automates it all so you can focus on code, not infrastructure.
• Hibernation: When you are done for the day or waiting on feedback? Just hit pause. With hibernation, you can preserve your state without incurring active GPU time costs until you're ready to resume.
• High-speed NVMe storage: Access your data right when you need it. High-speed NVMe storage ensures lightning-fast read/write speeds for training datasets, model checkpoints and large-scale inferencing
• AI Studio: The best part is that you can also build with Gen AI apps without touching infrastructure. Just go to the Hyperstack AI Studio. It’s everything you need, from fine-tuning and inference to evaluation and deployment in one environment.Try AI Studio today! 

Hyperstack Performance

Hyperstack is optimised for large-scale training and fine-tuning. With NVIDIA H100 and H200 SXM (featuring NVSwitch and NVLink), you can run multi-node training or inference-heavy workloads with minimal latency and massive bandwidth. Our NVMe-backed storage ensures bottlenecks don’t slow you down and the 350 Gbps high-speed networking (for NVIDIA A100, NVIDIA H100 PCIe and NVIDIA H100 SXM) eliminates the usual lag in distributed training.

Hyperstack Cloud GPU Pricing

Check out the pricing for cloud GPU rental on Hyperstack: 

2. Runpod 

Runpod offers a mix of centralised and community-hosted GPU nodes, making it a flexible and affordable option for developers who want control. It’s a favourite among AI enthusiasts and researchers who enjoy tinkering with environments or running containerised setups.

Key Features of Runpod

The key features of Runpod include:

• Community and secure cloud GPU instances
• Support for Docker-based containers
• Built-in auto-scaling and hibernation
• SSH and Jupyter Notebook access
• GPU marketplace with real-time availability
• Volumes for persistent storage

Runpod Performance

Runpod gives you access to a wide range of NVIDIA GPUs, depending on the node type — from community hosts to secure enterprise-grade instances. Latency and performance will vary depending on the provider you choose (community vs secure cloud, but the Docker-first experience and container orchestration make it highly adaptable.

Runpod Pricing 

Check out the pricing for cloud GPU rental on Runpod: 

3. Lambda Labs

Lambda is a well-known name in the AI community. It was initially famous for its GPU workstations. But they now offer a cloud GPU service that appeals to enterprises and research labs who need reliability and managed infrastructure.

Key Features of Lambda Labs

The key features of Lambda Labs include: 

• High-end GPU instances (H100, A100, V100)
• Pre-configured PyTorch, TensorFlow, and JAX environments
• Secure multi-tenant cloud with isolation
• SSH access with persistent volumes
• Fast EBS and NVMe-backed storage
• JupyterLab, VS Code Server access

Performance

With enterprise-grade GPUs and tightly managed environments, you can expect low-latency performance, high throughput and minimal downtime. You can also try the Lambda Cloud Metrics Dashboard to monitor your GPU workloads in real-time, without the need to build or manage your monitoring setup. Once you install the Lambda-guest-agent on your On-Demand instances or 1-Click Clusters, you can view essential system metrics right from your Lambda Cloud dashboard.

Lambda Labs Pricing

Check out the pricing for cloud GPU rental on Lambda Labs: 

4. Paperspace

Paperspace is all about ease of use. With a user-friendly interface, powerful notebooks and seamless integrations, it’s a great choice for solo developers, students and startups getting started with deep learning.

Key Features of Paperspace

The key features of Paperspace include: 

• Gradient Notebooks for fast prototyping
• One-click ML template setups
• Support for PyTorch, TensorFlow, Jupyter, etc.
• Persistent storage and auto-snapshot
• Pre-built Docker environments
• API + CLI for automated deployment

Performance

Paperspace is known for offering powerful GPUs at a fraction of the cost of traditional cloud providers. Performance generally lives up to the promise, especially for short to mid-term training runs and development. Its performance includes:

• Low-latency service with a RESTful API
• Solid performance with TensorFlow, PyTorch, and CUDA-based applications

Paperspace Pricing

Check out the pricing for cloud GPU rental on Paperspace: 

5. Vast.ai

Vast.AI is a global GPU marketplace built to slash the cost of cloud GPU rentals for AI, ML, and compute-heavy tasks. Using a sharing-economy model, it delivers significantly cheaper GPU access than traditional providers.

Key Features of Vast.ai

The key features of vast.ai include:

• Cheapest GPU pricing on the market
• Full control over the software stack
• Custom Docker image support
• Real-time bidding and pricing
• Transparent performance stats for each host
• Wide range of GPUs (from 1080 Ti to A100)

Performance

Because Vast.ai is a decentralised platform, performance depends heavily on the individual host. Some machines offer fast NVMe drives and low-latency networking; others may not. But if you’re willing to shop around and test, you can get serious computing at rock-bottom prices.

Vast.ai Pricing 

Check out the pricing for cloud GPU rental on Vast.ai: 

Conclusion

The best cloud GPU rental platform for you depends on your workload requirements, technical experience and scalability needs. If your focus is on building and deploying market-ready AI or just experimenting with the latest models, Hyperstack offers a real cloud environment for that. It's built to support demanding workloads such as training large language models, fine-tuning, and inference at scale with dedicated infrastructure, high-speed networking and the latest AI Studio platform.

So while there are strong use cases for each platform in this comparison, if your priority is speed, scalability and building real-world AI products, Hyperstack offers the right balance of tools and performance to get you there. 

Build Market-Ready Solutions with Hyperstack

FAQs

What is cloud GPU rental and why is it useful for AI workloads?

Cloud GPU rental lets you access powerful graphics processing units remotely, so you don’t need to buy expensive hardware. It’s especially useful for training large AI models, fine-tuning LLMs and running inference at scale without upfront costs.

How much does Hyperstack cloud GPU rental cost?

Hyperstack offers competitive, flexible pricing:

• H200 SXM: $3.50/hr
• H100 SXM: $2.40/hr
• A100 SXM: $1.60/hr
• L40: $1.00/hr
• A6000: $0.50/hr

You can choose on-demand, reserved or spot VMs depending on your workload and budget.

Can I fine-tune large language models with Hyperstack cloud GPUs?

Yes, Hyperstack is purpose-built for fine-tuning LLMs and training large models. It offers high-speed networking (350 Gbps) and powerful GPUs like H100 NVLink, H100 SXM and more, making it ideal for distributed training and multi-node setups.

Is there a way to pause my cloud GPU VMs on Hyperstack?

Yes, Hyperstack offers a hibernation feature that lets you pause idle workloads without losing progress. It helps you save costs while preserving the session state, ideal for long-running experiments or dev breaks.

Which GPU should I rent on Hyperstack for deep learning?

It depends on your workload. For training large models, go with the H100 SXM or H200 SXM. For cost-effective fine-tuning, the A100 SXM or A100 PCIe options work well. Lighter tasks? Try A6000 or L40.