Choosing the Right GPU for Your Needs

Choosing the GPU that best suits your needs can be challenging. Whether you're tackling AI and graphics-intensive tasks, focusing on universal computing at the data center level, or pushing the boundaries with exascale high-performance computing (HPC), it's crucial to understand the differences between various GPUs and how they might work for you.

Today, we're looking at two of NVIDIA's top-tier GPUs: the versatile L40S and the powerhouse H100. The L40S shines as a flexible choice, offering robust performance across AI and graphics workloads. Meanwhile, the NVIDIA H100 is the go-to GPU for those looking to achieve unparalleled power in the most demanding AI and HPC environments.

What are the main differences between the L40S and the H100? Which one is right for your needs? Let's explore these questions and more…

NVIDIA H100: The AI Titan

The H100 GPU made a big splash when it was released in March 2023. Major names in generative AI like OpenAI, Meta, and Stability AI adopted the H100 to accelerate their cutting-edge work. It's one of the very best GPUs on the market today – particularly well suited for AI and deep learning applications.

Built on the Hopper architecture, the H100 features fourth-gen Tensor Cores and a dedicated Transformer Engine to solve trillion-parameter language models. According to NVIDIA, it can speed up large language models (LLMs) by 30X over its predecessor, the A100 GPU. (We compared the H100 and A100 in a previous blog post here.)

A few of the H100's standout features include:

• Superior Tensor Core throughput performance, along with advanced Transformer Engine and FP64 and FP8 capabilities for next-gen AI workloads.
• Efficient scalability thanks to fourth-gen NVLink (with 900 GB/s of GPU-to-GPU interconnect), NDR Quantum-2 InfiniBand networking, and PCIe Gen5.
• GPU memory bandwidth of 3.35 TB/s to handle the most data-intensive tasks.
• Multi-Instance GPU (MIG) capability, with superior resource allocation and flexibility in multi-tenant environments or when running multiple workloads at the same time.
• Highly efficient sparsity support that doubles the performance for certain AI and machine learning tasks.

But the H100 does come with a price tag to match its game-changing capabilities – it's an investment for those who need the speed and power it delivers. For instance, if your projects involve massive datasets, large-scale simulations, or advanced AI training for foundational models, the H100 is certainly worth the consideration.

NVIDIA L40S: The Versatile Contender

The L40S, while not as specialized as the H100, excels in environments where flexibility is key. Built on the Ada Lovelace architecture, the L40S is designed to handle a wide range of workloads, making it a solid choice for everything from AI training to real-time graphics rendering.

Although it lacks FP64 performance, the L40S compensates with excellent FP32, mixed-precision performance, and Tensor Core capabilities. It also features advanced DLSS 3.0 and ray-tracing, plus DisplayPort outputs and NVENC / NVDEC with AV1 support, making it ideal for graphics-heavy applications.

Its 48MB GDDR6 memory isn't quite comparable to the H100's 80MB HBM3 memory, but it's perfectly respectable nonetheless. Also, its lower memory bandwidth of 864 GB/s (versus the H100's 3.35 TB/s) is something to consider if you're dealing with memory-intensive machine learning scenarios.

A point in favor of the L40S:

• It can be used as an Omniverse server and generates highly realistic 3D synthetic data for the most demanding workloads.
• Ideal for extended (XR) and virtual reality (VR) applications, design collaboration, and digital twins.

The L40S doesn't provide MIG support like the H100 does, but its strength lies in its ability to adapt to a variety of tasks, making it a strong all-around GPU for general computing, AI, and graphics workloads.

Which GPU Should You Choose?

For reference, here is a quick comparison of some of the features and specs of the L40S and H100 GPUs:

Ultimately, the choice between the L40S and H100 depends on your specific needs.

You may want to consider the L40S for:

• Cost-effective inference, given that it offers roughly 40% of the inference performance of the H100 at ~30% of the cost.
• Small-model training and fine-tuning, where the L40S provides sufficient performance without the need for the extensive capabilities of the H100.
• Versatile computing environments striking a balance between AI, graphics, and general compute tasks without the need for MIG capabilities.
• Graphics-heavy applications requiring advanced rendering and real-time processing.
• Prioritizing cost-efficiency and power efficiency, along with the convenient integration of a dual-slot PCIe form factor.

Whereas the H100 is better suited for:

• Training foundational models, as it was specifically designed to handle the scale and complexity needed.
• Fine-tuning medium to large models as a general rule, although the L40S can be a solid choice for mid-range needs depending on your specific requirements.
• Cutting-edge AI research, large-scale simulations, and other deep learning applications that require maximum computational power.
• HPC environments where speed, memory, bandwidth, and double-precision performance are critical.
• Intensive computational tasks and workloads that don't involve high-end graphics and media rendering.

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