Why Choose NVIDIA H100 SXM for LLM Training and AI Inference

The demand for strong hardware solutions capable of handling complex AI and LLM training is higher than ever. Whether building advanced conversational agents, generative AI tools or performing inference at scale, choosing the right GPU is imperative to ensure optimal performance and efficiency. The NVIDIA H100 SXM is a GPU designed to handle extreme AI and high-performance computing (HPC) needs. Explore how the NVIDIA H100 SXM manages extensive AI and LLM workloads in our latest blog.

Challenges in LLM Training and AI Inference

Here are the key challenges that come with training large-scale AI models and performing inference, such as:

• Data Bottlenecks: Large AI models require massive datasets, often causing data processing and transfer delays.
• Memory Limitations: Complex models require substantial memory, and inadequate memory can hinder performance or cause failures.
• Long Training Times: Training large models can be time-consuming, often taking weeks.
• High Computational Requirements: AI models require immense computational power, which can strain hardware resources during training.
• Latency Issues: Real-time AI applications face challenges with high latency during inference, affecting overall model performance.

Why Invest in High-Performance GPUs for Intensive Workloads

When tackling large-scale AI and LLM workloads, selecting the right GPU is essential to achieving optimal results. Here are five key reasons why strong hardware is critical for training and inference workloads:

• Enhanced Computational Efficiency: High-performance GPUs accelerate matrix-heavy operations, speeding up large dataset processing and reducing time spent on model training and fine-tuning.
• Faster Training and Inference Cycles: Powerful GPUs minimise bottlenecks, enabling faster training and inference on large AI models with minimal interruptions.
• Ability to Handle Complex Models: GPUs with high memory, bandwidth, and computational power efficiently handle complex LLMs, reducing the need for data swaps during training.
• Scalability and Flexibility: Multi-GPU support allows businesses to scale AI workloads and adapt to increasing demands without sacrificing performance.
• Cost-Effective Resource Management: Investing in high-performance GPUs lowers operational costs by improving training efficiency and reducing time-to-market.

Try our LLM GPU Selector to Find the Right GPU for Your Needs

Why Choose NVIDIA H100 SXM for LLM Training and AI Inference

Here’s why choosing the NVIDIA H100 SXM on Hyperstack could be an ideal choice for LLM training and AI inference:

1. Exceptional Memory Capacity for Large-Scale AI Models

The NVIDIA H100 SXM has 80 GB of HBM2e memory, offering high bandwidth and capacity to process massive datasets efficiently. This capability is especially vital for training and fine-tuning advanced LLMs with billions of parameters such as Llama 3 405B and Llama 3.1 70B  These models require substantial memory to execute seamlessly.

The memory's high bandwidth allows faster data access and processing for smoother handling of complex computations without slowdowns. So you can reduce training times and run larger batch sizes for higher model accuracy.

And that’s not all, NVIDIA H100 SXM’s memory capacity also ensures seamless deployment of inference workloads for real-time applications like chatbots and recommendation systems. Hyperstack enhances this advantage by providing persistent NVMe storage to ensure your datasets and training outputs remain accessible for future iterations.

Also Read: How to Optimise LLMs on Hyperstack

2. Unmatched Compute Power for Accelerated AI Training

AI workloads thrive on computational power and the NVIDIA H100 SXM delivers it with 1,984 Tensor Cores, optimised for matrix-heavy operations. These Tensor Cores enable faster training cycles for LLMs, reducing the time it takes to complete epochs on extensive datasets. This means significantly shorter project timelines without compromising model performance. Tensor Cores also offer flexibility with precision modes such as FP16 and FP32, balancing computational efficiency and accuracy for various AI applications.

We offer new-generation H100 SXM GPUs with tailored configurations [see the table below]. Our new generation flavours for NVIDIA H100 SXM GPU dramatically reduce deployment times and boost CPU and memory performance by 10-15%. These configurations are designed to maximise the utilisation of every Tensor Core, ensuring peak performance for demanding AI workflows. 

3. High-Speed GPU-to-GPU Communication with NVLink

The NVIDIA H100 SXM is equipped with NVLink which provides a direct interconnect between GPUs with an impressive P2P throughput of 745 GB/s, supported by the SXM5 architecture. This advanced connectivity ensures seamless data transfer between GPUs, making it ideal for efficient multi-GPU scaling which is essential for handling workloads that demand extensive computational power and parallel processing. This includes large AI model training, running distributed inference and accelerating complex simulations.

4. Optimised AI Inference for Real-Time Applications

The NVIDIA H100 SXM is designed not just for training but also for optimising AI inference. With a staggering 2,000 TOPS of AI inference performance, this GPU excels at deploying models in real-world applications like virtual assistants, autonomous systems, and personalised recommendations.

Our NVIDIA H100 SXM supports high-speed networking with up to 350 Gbps bandwidth for seamless data transfer and reduced latency for inference workloads. This optimisation is essential for meeting the fluctuating performance demands of real-time applications where low latency is critical. Read our blog on Improving LLM Fine-Tuning and Inference with High-Speed Networking 

Start Your AI Journey With Hyperstack. Sign Up Now to Access the NVIDIA H100 SXM.

You may also like to read:

• Comparing NVIDIA H100 PCIe vs SXM: Performance, Use Cases and More

• Introducing the NVIDIA H100-80GB-SXM5: Unparalleled Performance for AI and HPC Workloads

FAQs 

How does NVLink improve multi-GPU scaling in the NVIDIA H100 SXM?

NVIDIA H100 SXM’s NVLink enhances multi-GPU scaling by providing a high-speed, low-latency interconnect between GPUs for faster data transfer and synchronisation, crucial for large-scale AI model training and distributed inference.

What AI applications are ideal for NVIDIA H100 SXM GPUs?

NVIDIA H100 SXM GPUs are perfect for AI applications that require immense compute power, such as large language model (LLM) training, generative AI tools, autonomous systems, and real-time AI inference applications.

How does Hyperstack optimise NVIDIA H100 SXM for my workloads?

Hyperstack’s platform optimises NVIDIA H100 SXM GPUs with high-speed networking, persistent storage, and tailored VM configurations to maximise performance, minimise latency, and streamline deployment for AI and HPC workloads.