5 Best GPUs for AI in 2025: All You Need to Know

With new releases now and then, AI models get bigger, training gets more complex and inference workloads become more demanding. But here's the catch: not all GPUs can keep up with the demands.

Choosing the wrong one can slow down your progress, eat into your budget or worse, affect your model performance. That's why you need the right GPU recommendations for AI tailored to your workload. 

No matter what workload you're running, be it fine-tuning LLMs, running vision tasks or building real-time AI applications, choosing the right GPU matters. And not just any GPU, choose the one built for your specific AI workload. 

Continue reading as we break down the best GPUs for AI in 2025.

1. NVIDIA H200 SXM

The NVIDIA H200 SXM is built for cutting-edge AI at scale. With 141GB of next-gen HBM3e memory, it enables large models like Llama 3.3 70B, Llama 3.1 70B  or similar foundation models to be trained and run entirely in-memory, minimising memory bottlenecks and eliminating the need for slower external caching. 

What sets the NVIDIA H200 SXM apart is its raw compute power, offering up to 3,958 TFLOPS of FP8 performance, nearly double that of the NVIDIA H100 GPUs. This makes it ideal for:

• Pre-training massive transformer architectures on trillion-token datasets for next-generation language models requires distributed parallel computing power.
• Training large-scale vision transformers, object detection models and image segmentation networks on high-resolution datasets and video streams.
• Processing concurrent NLP workloads including sentiment analysis, entity recognition, summarisation and translation across multiple languages simultaneously.
• Serving high-throughput inference for text-to-image, image-to-video and text generation models with consistent sub-second response times

Even better? With Hyperstack NVIDIA H200 SXM VM, you get high-speed networking of up to 350 Gbps for low-latency and high-throughput workloads. This also means you get faster training cycles and improved throughput during inference.

2. NVIDIA H100 SXM

The NVIDIA H100 SXM is one of the most popular GPUs used for AI workloads. This GPU offers an ideal balance between ultra-high performance and cost-efficiency, making it a top choice for enterprises building and deploying AI at scale. Powered by 80GB of HBM3 memory and over 1,900 Tensor Cores, the NVIDIA H100 SXM excels at AI model training with hundreds of millions to tens of billions of parameters.

Built on the SXM5 form factor, the NVIDIA H100 SXM also supports 600 GB/s NVLink bandwidth for seamless GPU-to-GPU communication, crucial for distributed training and multi-GPU workloads such as:

• Training large transformer models such as GPT-4, Llama 3.1/3.3 or Mistral where model and data parallelism are required to manage billions of parameters across GPUs.

• Fine-tuning instruction-tuned models like Llama 2 Chat or Gemma for domain-specific enterprise applications such as financial forecasting, legal document processing or healthcare summarisation.

• Vision-language training for models like CLIP, BLIP-2 or Flamingo, where image and text data are fused to power multi-modal applications such as product search or smart content generation.

• Distributed training of diffusion models like Stable Diffusion for high-resolution image or video generation.

You May Also Like to Read: Why Choose NVIDIA H100 SXM for LLM Training and AI Inference

3. NVIDIA H100 PCIe

For those looking to get high performance for large-scale AI training or similar workloads, the NVIDIA H100 PCIe could be your go-to choice. It maintains the same core architecture as the NVIDIA H100 SXM with HBM3 memory, 1,984 Tensor Cores and strong FP64 and inference performance but uses a PCIe interface. This GPU is ideal for AI workloads such as:

• Training 7 B-70 B parameter models like Llama, Mistral and Code Llama for specialised enterprise applications.
• Building cost-effective multi-node training clusters for organisations requiring scalable AI infrastructure without premium SXM interconnect costs.
• Fine-tuning foundation models for specific tasks like medical diagnosis, legal document analysis and customer service automation applications.

Still Confused Between NVIDIA H100 SXM and NVIDIA H100 PCIe? No problem, check out our comparison here to learn the difference. 

4. NVIDIA A100 PCIe

Although it’s based on a previous-generation architecture, the NVIDIA A100 PCIe still delivers outstanding performance, especially for teams with tighter budgets or those focused on smaller-scale training and inference. With 80 GB of HBM2e memory and 432 Tensor Cores, it is an ideal choice if you need amazing performance at an even amazing (and lower) price. It is one of the best affordable GPUs for AI development today

• Training 1B-13B parameter models for startups and small teams developing specialised AI applications cost-effectively.
• Rapid prototyping of machine learning models, testing new algorithms and iterating on AI concepts without high-end hardware investments.
• Running inference for chatbots, recommendation systems and computer vision applications in on-premises environments with budget constraints.
• Building initial AI infrastructure for early-stage companies needing proven performance while managing capital expenditure and operational costs effectively.
  

Need more power for distributed workloads? We’ve got you covered. We also offer the NVIDIA A100 PCIe with NVLink that boosts GPU-to-GPU bandwidth for higher performance for intensive training and inference.

Learn more about choosing NVIDIA A100 PCIe for your workloads in our blog here!

5. NVIDIA L40

The NVIDIA L40 might fly under the radar compared to more widely recognised cloud GPUs for AI but for teams working in AI-driven 3D simulation, rendering or virtualisation, it’s powerful and budget-friendly. Built on NVIDIA’s Ada Lovelace architecture, the NVIDIA L40 offers 48 GB of GDDR6 ECC memory and 568 fourth-gen Tensor Cores, making it ideal for tasks like real-time AI applications.

The NVIDIA L40 GPU is cost-effective and ideal for AI workloads such as:

• Training NeRF models for photorealistic rendering and spatial AI applications.
• Running real-time object detection, semantic segmentation and pose estimation workloads for autonomous systems and surveillance applications.
• Serving text-to-3D and procedural content generation models for interactive design tools and creative AI workflows.
• Training vision-language models that combine 3D spatial understanding with text processing for robotics and augmented reality applications.

Check out how the NVIDIA L40 Accelerates AI Training! 

Why Deploy Cloud GPUs for AI on Hyperstack

Choosing the right GPU is only part of the equation. Where you deploy it matters just as much. That’s where Hyperstack comes in. Our infrastructure is purpose-built for AI, offering the performance you need to get the most out of your AI workloads:

• NUMA-Aware Scheduling and CPU Pinning: Get to improve performance for parallel jobs and latency-sensitive AI inference tasks by aligning compute workloads with memory and CPU topology.
• High-Speed Networking: Get up to 350 Gbps network throughput on supported GPUs like the NVIDIA A100 PCIe, NVIDIA H100 PCIe and NVIDIA H100 SXM, ideal for distributed AI training and multi-GPU inference pipelines.
• Ultra-Fast NVMe Storage: Access fast local storage ranging from 6.5 TB to 32 TB, depending on your configuration, so you don’t run into I/O bottlenecks during AI model training or data pre-processing.
• Hibernation Options: Pause your AI workloads when not in use to reduce operational costs and maximise efficiency.

Conclusion

Choosing the right GPU for your AI workloads requires more than just comparing TFLOPS or memory specs. It’s about aligning your infrastructure with the specific requirements of your AI models. Hyperstack provides production-grade GPU infrastructure optimised for a wide range of AI workloads, from generative AI and LLM training to real-time inference.

New to Hyperstack? Try our ultimate cloud GPU platform today and run your choice of GPU that fits your needs.

FAQs

Are PCIe GPUs good for AI workloads?

Yes, PCIe GPUs like the NVIDIA A100 and NVIDIA H100 offer excellent performance for single-node or budget-conscious training and inference workflows.

How does NVLink benefit AI training?

NVLink allows high-bandwidth GPU-to-GPU communication, which reduces latency and improves performance for large distributed training or inference tasks.

Does Hyperstack support custom OS images?

Yes, Hyperstack lets you load pre-configured OS images for consistent environments and faster deployment across all your AI projects. Learn more here. 

How much does the NVIDIA H100 SXM VM cost on Hyperstack?

The NVIDIA H100 SXM VM is for $2.40 per hour. Access on-demand here!

What is the price of the NVIDIA A100 PCIe on Hyperstack?

The NVIDIA A100 PCIe VM costs $1.35 per hour, making it ideal for budget-friendly training, inference and fine-tuning workloads.