Powerful Computational Hardware for AI and Machine Learning

Powerful computational hardware is necessary for the training and deployment of machine learning (ML) and artificial intelligence (AI) systems. The parallelism and computational power of the GPU make it a critical component for machine learning models.

NVIDIA is at the forefront of GPU development for deep learning, propelled by the growing complexity of machine learning models. The NVIDIA H100 is built on the Hopper architecture. It’s designed to break new ground in computational speed, tackling some of AI’s most challenging and high-performance computing (HPC) workloads.

This article will compare NVIDIA H100 with other popular GPUs in terms of performance, features, and suitability for various machine learning tasks.

Prerequisites

Basic understanding of machine learning concepts, familiarity with GPU architectures, and knowledge of performance metrics like FLOPS and memory bandwidth will help to better appreciate the comparisons between the H100 and other GPUs.

Unveiling the NVIDIA H100

The NVIDIA H100 is a revolutionary GPU that leverages the success of its predecessors. The GPU is packed with features and capabilities to enable new levels of high-performance computing and artificial intelligence. Let’s consider its key features and innovations:

Architecture and Performance

Based on NVIDIA’s Hopper architecture, the H100 offers 80 billion transistors of TSMC’s 4N process, up to 16,896 FP32 CUDA cores, and 528 fourth-generation Tensor Cores in the SXM5 version.

Memory and Bandwidth

Another feature is its HBM3 memory, which can reach as high as 80GB in capacity, with bandwidth set at 3.35 TB/s on the SXM5 version. Large memory and high bandwidth are essential for handling massive datasets and complex models.

Tensor Cores and AI Performance

The Tensor Cores in the H100’s fourth generation show huge advancements for AI workloads. It supports the FP8 precision mode that results in up to 9x faster AI training than the previous generation.

Interconnect and Scalability

The H100 supports PCIe Gen 5 with 128 GB/s bidirectional bandwidth. It also features fourth-generation NVLink with up to 900 GB/s of bidirectional throughput, enabling the rapid scaling of workloads across GPUs and nodes.

Comparing H100 with Other GPUs

Comparing NVIDIA H100 and A100

Driven by the NVIDIA Ampere architecture, the NVIDIA A100 is an accelerator tailored to AI. It delivers a paradigm-shifting improvement in the performance of AI workloads, from deep learning to data analytics.

NVIDIA A100 can be partitioned into up to seven instances using a process called multi-instance GPU (MIG) for better distribution of workloads. It also has 40 GB or 80 GB of high-bandwidth memory, enabling it to work with large models.

The A100 supports mixed-precision computing and Tensor Cores that provide precision and speed. It also features NVLink 3.0 for fast communication between multiple GPUs and scale-out performance in demanding environments.

AI Inference PerformanceUp to 30x faster than LLMsBaselineSpecial FeaturesTransformer Engine, DPX InstructionsMulti-Instance GPU (MIG)

While the A100 is still a powerful GPU, the H100 brings significant improvements. With its additional Transformer Engine and support for FP8 precision, it’s best for large language models and architectures based on transformers.

Note: In this context, “Baseline” refers to the standard performance level of the NVIDIA A100. It serves as a reference to illustrate how much faster the NVIDIA H100 is relative to the A100.

Comparing NVIDIA H100 and RTX 4090

The hardware specs related to RTX 4090 are impressive. It includes 16,384 CUDA Cores, 512 fourth-generation Tensor Cores, and 24GB GDDR6X memory. Additionally, it offers a memory bandwidth of 1 terabyte per second (TB/s).

The RTX 4090 delivers up to 330 TFLOPS of FP16 Tensor performance, thanks to a new pipeline optimized for DLSS 3. Its advanced ray tracing technologies enhance fidelity and efficiency in graphics-intensive workloads.
The table below highlights the key differences between NVIDIA H100 and RTX 4090.

The RTX 4090 offers excellent performance for its price. However, its primary design focus is on gaming and content creation. The H100 has a larger memory capacity and higher bandwidth. It also includes features designed for heavy-duty AI and HPC tasks.

Comparing NVIDIA V100 vs. H100

The NVIDIA V100, leveraging the Volta architecture, is designed for data center AI and high-performance computing (HPC) applications. It features 5,120 CUDA Cores and 640 first-generation Tensor Cores. The memory configurations include 16GB or 32GB of HBM2 with a bandwidth capacity of 900 GB/s.
Achieving up to 125 TFLOPS of FP16 Tensor performance, the V100 represented a significant advancement for AI workloads. This powerhouse uses first-generation Tensor Cores to accelerate deep learning tasks efficiently. Let’s consider the table below that compares the NVIDIA V100 with H100

The H100 significantly outperforms the V100, offering much higher compute power, memory capacity, and bandwidth. These architectural improvements and specialized features enhance its suitability for modern AI workloads.

Performance Comparison: Training and Inference

One of the key factors in selecting a GPU is to find the right balance between training and inference performance. The performance of GPUs can vary significantly based on the type of model being used, the dataset size, and the specific machine learning task. GPUs can perform quite differently depending on the specific model type. Thus, the choice of the right one will depend on the requirements of the workload.

NVIDIA H100 vs A100 vs V100: Comparing Performance for Large-Scale AI Model Training

NVIDIA H100 can achieve the highest throughput for training large models such as GPT-4, BERT. It’s optimized for high-performance computing and advanced artificial intelligence research. In addition, it supports massive amounts of data and deep models with a large number of parameters.

The A100 is also great for training large models, though it doesn’t quite match the H100’s performance. With 312 TFLOPS for tensor operations and 2 TB/s memory bandwidth, it can handle massive models but with longer training times than the H100.

On the other hand, the V100 uses an older architecture. While it can be used to train large models, its low memory bandwidth and tensor performance of 125 TFLOPS make it less suitable for next-generation AI models.

It’s a good choice for AI researchers and developers for experimentation and prototyping but lacks the enterprise-level features of the H100 and A100.

NVIDIA H100 vs A100 vs V100 vs RTX 4090: Inference Performance and Scalability with Multi-Instance GPU (MIG) Capability

Both the H100 and A100 perform very well with multi-instance GPU (MIG) capability, which enables inference tasks to run simultaneously. The H100 can be partitioned into multiple instances as opposed to the A100, making it more scalable for large-scale deployments.

Let’s have a look at the landscape of GPU architectures designed for inference tasks. When evaluating options, we encounter several prominent contenders:

• H100: It’s well-suited to inferencing tasks, such as serving models in production or running inference across many jobs or users.
• A100: Outstanding at inference with a particular focus on scalability and efficient use of resources. It comes with the MIG technology, though it supports fewer instances than the H100.
• V100: Good for running inference for moderate models but lacks the scalability and partitioning features of the A100 and H100.
• RTX 4090: Best for small-scale inference, such as research, and development, but it lacks the enterprise-grade features necessary for large-scale deployment.

Balancing Cost and Performance: Choosing the Right GPU for AI Tasks

Cost is another consideration when selecting a GPU. The cost will depend on the features and performance we’re looking for. Although the H100 is the cutting edge of current technology, it’s also the most expensive system designed for enterprise-level applications.

Let’s see how the cost behave for different GPUs based on their use cases and target audiences:

• H100: Most expensive, sometimes costing tens of thousands of dollars per GPU, for use by companies that conduct advanced AI research and development.
• A100: It’s cheaper than the H100, but still expensive, and offers strong performance for many AI tasks. It’s often found in cloud environments.
• V100: It’s less expensive than H100 and A100 but also a decent option for companies with smaller budgets that still require strong AI performance.
• RTX 4090: It’s the most affordable option, typically costing a fraction of enterprise GPUs.

Choosing the Right GPU: Tailoring Performance and Budget for AI Workloads

The GPU we choose depends on the workload, budget, and scalability required. GPUs can perform differently depending on the specific model type and the nature of the tasks being executed. Consequently, it’s essential to match the GPU with our project needs.

NVIDIA H100 is designed for large enterprises, research institutes, and cloud providers. These organizations would benefit from its performance to train massive AI models or perform high-performance computing. It offers the largest selection of modern AI techniques, with the additional features required for training machine learning models, inference, and data analytics tasks.

For any organization that doesn’t need bleeding-edge performance, the A100 is a great choice. It’s fast for AI training or inference workloads that benefit from multi-instance GPU (MIG) technologies. This enables the partitioning of resources for multiple users. It’s well-suited to an environment that maximizes efficiency, such as cloud environments.

For a moderate workload, the NVIDIA V100 GPU is a cost-effective solution that can get the task done. It’s not as powerful as the H100 or the A100, but it still delivers enough performance at a lower price point.

The RTX 4090 is best suited for developers, researchers, or small organizations that need a powerful GPU for AI prototyping, small-scale model training, or inference. It offers impressive performance for its price, making it an excellent choice for those working on a budget.

Summary Table: GPU Selection Based on Workload, Budget, and Scalability

Conclusion

Choosing the right GPU is especially important in the fast-moving world of AI and machine learning since it impacts the productivity and scalability of the model. The NVIDIA H100 is a great choice for organizations on the cutting edge of AI research and high-performance computing.

However, depending on our needs, other options like the A100, V100, or even the consumer-grade RTX 4090 can deliver strong performance at a lower cost.

By carefully examining our machine learning workloads and analyzing the strengths of each GPU, we can make an informed decision. This will ensure the best combination of performance, scalability, and budget.