NVIDIA H200 vs. NVIDIA L40: When to choose which

GPU cloud services deliver high-performance computing capabilities with specialized infrastructure for AI and machine learning workloads. Users get access to GPU clusters connected through high-bandwidth networks, enabling distributed processing and faster model training. These services include pre-configured environments optimized for common AI frameworks, reducing setup time and complexity.

The infrastructure scales based on demand, from single GPU instances to multi-GPU clusters. Features include low-latency networking and high-speed interconnects. Security measures, compliance certifications, and technical support are standard offerings. Pricing models are usage-based, with costs varying by GPU type, usage duration, and resource allocation.

About the Nvidia H200

The NVIDIA H200 delivers a significant step forward in GPU memory capacity. It offers 141GB of HBM3e memory with substantially higher bandwidth than its predecessor. The H200 nearly doubles the memory of the H100 while providing 1.4x the bandwidth. This makes it particularly valuable for memory-intensive AI workloads. The extra memory allows researchers to work with larger models without hitting memory bottlenecks that often slow training and inference.

AI researchers working on the largest language models and computer vision projects choose the H200 when they need extra memory headroom. It appeals to teams training massive models that won't fit comfortably on other GPUs. It's also ideal for high-performance computing applications that demand serious memory bandwidth. The main limitation is availability. Like most cutting-edge hardware, getting H200s can be challenging. Many organizations end up on waiting lists or pay premium rates through cloud providers who secured inventory.

About the Nvidia L40

The NVIDIA L40 is a versatile GPU designed for AI inference and graphics work. With 48GB of memory and solid performance, it's built for running trained AI models rather than training new ones from scratch. It excels at running vision models and generative AI applications. However, it's not the best choice for training massive models.

Researchers and companies working with computer graphics, virtual environments, and AI inference choose the L40. It appeals to organizations that need to deploy AI models in production or handle visual computing tasks. The GPU draws less power than training-focused alternatives. This makes it practical for companies that want to run AI applications efficiently rather than push the absolute boundaries of model development.

Comparison

• The NVIDIA H200 offers exceptional memory capacity at 141GB HBM3e with high bandwidth. This makes it ideal for training and inference of large AI models and HPC workloads. However, it comes with a premium price point of $30,000-$40,000 and may face availability constraints.
• The NVIDIA L40 provides a more cost-effective solution at around $11,000 with decent performance for inference tasks and generative AI applications. Its limitations include lower memory capacity at 48GB GDDR6 and reduced suitability for intensive training workloads due to lower memory bandwidth.

The NVIDIA H200 is best suited for enterprises, research institutions, and organizations requiring maximum performance for large-scale AI model training and memory-intensive HPC applications. Its substantial memory capacity and bandwidth make it ideal for teams working with cutting-edge AI models that demand extensive computational resources.

The NVIDIA L40 is more appropriate for smaller businesses, startups, and developers focused primarily on AI inference, generative AI applications, and mixed AI-graphics workloads. Its lower cost and power efficiency make it accessible for organizations with budget constraints or those prioritizing operational efficiency over maximum performance.

Frequently asked questions

What is the retail price difference between the NVIDIA H200 and L40?

The NVIDIA H200 costs approximately $30,000–$40,000, while the NVIDIA L40 costs around $11,000. This makes the H200 roughly 3-4 times more expensive than the L40.

How much memory does each GPU have and what type?

The NVIDIA H200 has 141 GB of HBM3e memory, while the NVIDIA L40 has 48 GB of GDDR6 memory. The H200 offers nearly triple the memory capacity.

What are the main use cases for each GPU?

The NVIDIA H200 is best for training and inference of large AI models and HPC workloads requiring high memory bandwidth. The NVIDIA L40 is optimized for inference of generative AI, vision models, and virtual environments.

What are the rental costs per hour for these GPUs?

The NVIDIA H200 costs approximately $3.83–$10 per hour to rent, while the NVIDIA L40 costs around $1.00 per hour. This makes the L40 significantly more affordable for hourly usage.

What are the key limitations of each GPU?

The NVIDIA H200 may have limited availability despite its superior performance capabilities. The NVIDIA L40, while having lower power draw, is less ideal for full-scale training due to lower memory bandwidth compared to the H200.

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