How to Rent GPU for AI in 2025: Your Complete Guide to Powering AI Workloads

Meta’s latest Llama 4 model was trained on a massive GPU cluster of over 100,000 NVIDIA H100 GPUs, a scale “bigger than anything” reported by competitors. Even previous models like Llama 3 used clusters of 16,384 H100 80 GB GPUs to train the model. 

If giants like Meta need such scale just to train open-source models, it’s clear that AI startups and even individuals need high-performance GPUs for modern AI workloads, be it generative AI, LLMs or real-time inference. But you don’t need to build your own cluster. Renting GPU resources smartly can give you the power you need without the long-term cost and complexity.

In our latest blog, we discuss why renting is a better option for AI workloads and how to rent gpu for AI in 2025.

Why Renting a GPU for AI Makes Sense vs. Buying

You might think: Why rent when I can own? On paper, buying might seem like a one-time investment but the reality is more nuanced. Here’s why:

• Pay for What You Use: Renting GPUs means you only pay for the hours you actually use. If your workload is irregular like bursts of training then idle periods, you won’t be stuck with expensive hardware sitting idle.
• Access to the Latest Hardware: Today’s flagship might be tomorrow’s mid-tier as advanced GPUs launch. Renting gives you instant access to cutting-edge GPUs like the H200 SXM or H100 SXM without the upfront cost of buying.
• Scalability on Demand: Need 1 GPU today and 64 tomorrow? With cloud GPU rentals, you can scale instantly without waiting for procurement or installation.
• No Maintenance Overhead: Forget about infrastructure management. The provider handles it all.
• Faster Time-to-Market: With optimised infrastructure, you can go from concept to production in hours, not weeks.

What You Must Consider Before Renting a GPU

Renting a GPU is not just about choosing the card with the biggest TFLOPS or VRAM number. Here’s what to look for:

A. The GPU Model Itself

Different workloads have different needs:

• Training massive LLMs: You’ll want top-tier GPUs with high memory bandwidth like the H200 SXM, H100 SXM or RTX 6000 Pro.
• High-throughput inference: Lower VRAM may be fine but you’ll still want low-latency interconnects.
• Fine-tuning smaller models: More affordable GPUs like the RTX A6000 Pro may not perform well.

B. Infrastructure Optimisation

Even the best GPU underperforms without the right supporting infrastructure. Look for:

• High-speed networking for minimal latency.
• Fast storage like NVMe or high-performance shared file systems for quick data access.
• NVLink support for fast GPU-to-GPU communication.

C. SLA and Uptime Guarantees

For mission-critical AI workloads, you cannot afford downtime. Check whether the provider offers a formal Service Level Agreement (SLA) with uptime guarantees (e.g., 99.9%+). An SLA-backed commitment ensures you’re compensated or protected if service drops below the agreed standard.

D. Deployment 

How quickly can you spin up and deploy? If a platform’s UI forces you through complex CLI setups for basic tasks, your time-to-market goes down. Look for providers with 1-click VM deployment and pre-configured environments so you can go from zero to running workloads in minutes, not hours.

E. Transparent Pricing

You must watch for hidden charges like data egress fees or costly storage tiers.

How to Rent a GPU for AI 

Here's how to rent a cloud GPU for AI:

Step 1: Find a High-Performance Cloud GPU Provider

The provider you choose will define your experience. While hyperscalers are great but they can be costly and overcomplicated for AI workloads. Hyperstack offers a production-grade GPU infrastructure optimised for AI workloads. With features like high-speed networking, NVMe storage, on-demand Kubernetes and 1-click deployment, Hyperstack offers a fast path from idea to execution.

Step 2: Review the GPU Options and Pricing

Check out the best GPUs available for AI offered by the provider, including their pricing: 

Step 3: Match the GPU to Your Workload

Consider your workload requirements before choosing any GPU for AI:

• You’re training a 70B parameter Llama 3 or DeepSeek R1 model, then go for H200 SXM or H100 SXM for optimal training speed and memory handling.
• You’re working with OpenAI’s latest GPT-OSS models, so choosing an H100 PCIe offers great performance at a slightly lower cost than H100 SXM.
• You’re fine-tuning a 7B parameter model or doing mid-sized computer vision training, so opt for A100 PCIe to balance cost and performance.
• You need to run real-time generative image workloads, then go for the RTX 6000 Pro for low-latency performance at a good price point.
• You need to run small-scale inference or experiment with smaller models, then the RTX A6000 is the ideal option with the lowest pricing.

Step 4: Rent a GPU for AI on Hyperstack

Renting on cloud GPU for AI on Hyperstack is straightforward:

1. Create Your Hyperstack Account: Go to the Hyperstack registration page and sign up using email or SSO (Google, Microsoft, GitHub).
2. Sign In and Activate: Use your login credentials to access Hyperstack and complete your billing information.
3. Add Credit: Add funds to your account to enable cloud GPU VM creation.
4. Deploy a New Virtual Machine
   
   
   • Go to Virtual Machines.
   • Click Deploy New Virtual Machine.

5. Choose a Flavor: A “flavor” defines your hardware configuration including GPU model, number of GPUs, CPU, RAM and storage. Pick what matches your AI workload.

6. Launch and Configure: Deploy the VM, set up your AI environment and start running your AI workloads.

And that’s how you rent a GPU for AI and start training or run inference on enterprise-grade infrastructure on Hyperstack.

Conclusion

Choosing the right GPU for AI in 2025 is no longer just about compute power. Users now look for peak performance, cost and infrastructure for their specific workloads. From budget-friendly options like the RTX A6000 to powerful models like the H100 SXM and H200 SXM, the right choice can accelerate development and reduce operational costs. 

Hyperstack lets you build AI projects faster and smarter with our high-performance cloud environment. If you need help getting started on Hyperstack, here are some helpful resources that will help you deploy your first VM on Hyperstack:

• Check out the Hyperstack API Documentation 

• Explore the Quick Platform Tour

• Need help? Contact us anytime at support@hyperstack.cloud  

FAQs

What are the best GPUs for AI in 2025?

NVIDIA H200 SXM and H100 SXM deliver top performance for large-scale training and high-throughput inference in demanding AI workloads.

Which is the most affordable GPU for AI?

NVIDIA RTX A6000 offers decent performance for fine-tuning and inference at a lower cost than high-end training GPUs.

What is AI GPU pricing?

Pricing on Hyperstack for cloud GPUs for AI ranges from $1.35/hour for A100 PCIe to $3.50/hour for H200 SXM, depending on model.

How to rent a GPU for AI on Hyperstack?

Create an account on Hyperstack here, add credit, select a GPU flavour, deploy a VM, configure your environment and start workloads.

Can I run large LLMs on affordable GPUs?

Smaller LLMs can run on A100 PCIe or RTX A6000 but massive models require H100 or H200.