A100/H100 is too expensive, why not use 4090?

1. NVIDIA H100

The Nvidia H100 is a high-performance GPU designed specifically for AI, machine learning, and high-performance computing tasks. It is based on Nvidia's Hopper architecture and features significant advancements over previous generations. Its key features include:

- Hopper Architecture: With 4th generation Tensor Cores, it delivers significantly higher AI training and inference performance compared to previous architectures.

- High Performance: The H100 offers up to 9x better training and 30x better inference performance compared to the A100, thanks to its advanced architecture and enhanced cores.

- Transformer Engine: The H100 includes a specialized engine to accelerate transformer model training and inference, crucial for NLP and other AI tasks.

- Higher Memory Bandwidth: The H100's memory bandwidth (2.0-3.0 TB/s) significantly exceeds the A100's 1.6 TB/s, allowing for faster data processing.

- Energy Efficiency: Despite higher performance, the H100 is designed to be more energy-efficient, potentially reducing operational costs over time.

- Enhanced Security: The H100 includes advanced security features to protect sensitive data during computation.

The H100 PCIe 80 GB is a professional graphics card by NVIDIA, launched on March 21st, 2023. Built on the 5 nm process, and based on the GH100 graphics processor, the card does not support DirectX. Since H100 PCIe 80 GB does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games.

2. NVIDIA A100

The Nvidia A100 is a high-performance GPU designed for AI, machine learning, and high-performance computing tasks. Based on the Ampere architecture, it is widely used in data centers for large-scale AI and scientific computing workloads. Its key features include:

- Ampere Architecture: The A100 is based on NVIDIA's Ampere architecture, which brings significant performance improvements over previous generations. It features advanced Tensor Cores that accelerate deep learning computations, enabling faster training and inference times.

- High Performance: The A100 is a high-performance GPU with a large number of CUDA cores, Tensor Cores, and memory bandwidth. It can handle complex deep learning models and large datasets, delivering exceptional performance for training and inference workloads.

- Enhanced Mixed-Precision Training: The A100 supports mixed-precision training, which combines different numerical precisions (such as FP16 and FP32) to optimize performance and memory utilization. This can accelerate deep learning training while maintaining accuracy.

- High Memory Capacity: The A100 offers a massive memory capacity of up to 80 GB, thanks to its HBM2 memory technology. This allows for the processing of large-scale models and handling large datasets without running into memory limitations.

- Multi-Instance GPU (MIG) Capability: The A100 introduces Multi-Instance GPU (MIG) technology, which allows a single GPU to be divided into multiple smaller instances, each with dedicated compute resources. This feature enables efficient utilization of the GPU for running multiple deep learning workloads concurrently.

The A100 PCIe 40 GB is a professional graphics card by NVIDIA, launched on June 22nd, 2020. Built on the 7 nm process, and based on the GA100 graphics processor, the card does not support DirectX. Since A100 PCIe 40 GB does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games.

3. NVIDIA RTX 4090

The Nvidia RTX 4090 is a high-end graphics card from Nvidia's GeForce RTX 40 series, based on the Ada Lovelace architecture. It is designed to provide exceptional performance for both gaming and professional creative applications. Key features include:

- Ampere Architecture: The Nvidia RTX 4090 is built on the Ada Lovelace architecture, which brings improved ray tracing, advanced tensor cores, enhanced performance and efficiency. It's optimized for AI-driven applications and workloads.

- Improved Ray Tracing: Third-generation RT cores enhance real-time ray tracing performance, providing more realistic lighting and shadows in games and applications.

- Advanced Tensor Cores: Fourth-generation Tensor Cores support DLSS 3.0, boosting AI-powered upscaling and rendering techniques for higher frame rates.

- Enhanced Performance and Efficiency: The architecture offers significant improvements in processing power and power efficiency compared to previous generations.

- Support for Advanced AI Features: Optimized for AI-driven applications and workloads, making it versatile for both gaming and professional use.

The GeForce RTX 4090 is an enthusiast-class graphics card by NVIDIA, launched on September 20th, 2022. Built on the 5 nm process, and based on the AD102 graphics processor, in its AD102-300-A1 variant, the card supports DirectX 12 Ultimate. This ensures that all modern games will run on GeForce RTX 4090.

Technical Analysis and Application Scenarios

1. H100: Excellent High-Performance Computing and Deep Learning Graphics Card

As NVIDIA's latest generation flagship graphics card, H100 has an amazing 1979 Tflops Tensor FP16 computing power and 989 Tflops Tensor FP32 computing power.

This makes it particularly outstanding in handling complex deep learning tasks.

Its 80 GB large-capacity video memory and 3.35 TB/s memory bandwidth can quickly process massive data, while 900 GB/s communication bandwidth and ~1 us low communication latency ensure efficient data transmission.

Application scenarios:

Deep learning model training: The high computing power and large bandwidth of H100 are very suitable for training large deep learning models, especially in the fields of natural language processing (NLP) and computer vision (CV).

Scientific computing and simulation: Scientific research and engineering simulation in the field of high-performance computing (HPC), such as climate modeling and drug development, can benefit from the powerful performance of H100.

Large-scale data analysis: For tasks that require processing and analyzing large-scale data sets, such as financial analysis and genomics, H100 provides sufficient computing power and storage bandwidth.

2. A100: An efficient solution that balances performance and cost

A100 is the predecessor of H100. Although its performance is slightly inferior, its 312 Tflops of Tensor FP16 computing power and 156 Tflops of Tensor FP32 computing power are still very powerful. The same 80 GB video memory and 900 GB/s communication bandwidth as H100 make it still very cost-effective in many application scenarios.

Application scenarios:

Deep learning inference: For trained deep learning models, A100 performs well in the inference stage and can quickly respond to and process a large number of inference requests.

Data center workloads: A100 can support a variety of workloads in the data center, including AI, data analysis, and traditional HPC tasks.

Cloud computing platform: Due to its relatively low cost, A100 has become the preferred graphics card for many cloud service providers to build efficient cloud computing platforms.

3. 4090: A cost-effective choice for gaming and lightweight computing

The 4090 is NVIDIA's high-end graphics card for the gaming and consumer markets, with 330 Tflops of Tensor FP16 computing power and 83 Tflops of Tensor FP32 computing power. Although its performance is not as good as H100 and A100, its 24 GB video memory and 1 TB/s memory bandwidth are sufficient for many applications. The 64 GB/s communication bandwidth and ~10 us communication latency also meet the needs of most non-high-performance computing tasks.

Application scenarios:

High-end games: The 4090 is designed for high-end games and can provide a smooth gaming experience at 4K resolution.

Video editing and rendering: Tasks such as video editing and 3D rendering require high graphics processing capabilities, and the 4090 can complete these tasks efficiently.

Lightweight AI tasks: For some AI tasks that do not require ultra-high computing power, such as image classification and object detection, the 4090 is also a good choice.

Comprehensive analysis of performance and application

From the above comparison and application scenarios, it can be seen that H100, A100 and 4090 each have their own unique advantages and applicable scenarios.

As a top-level graphics card, H100 is suitable for tasks that require the highest performance,

while A100 finds a balance between performance and cost and is suitable for a wide range of application scenarios.

Although 4090 is mainly aimed at the gaming market, its strong performance can also handle many professional tasks.

1. Performance advantage

H100: Its extremely high Tensor computing power and memory bandwidth make it unmatched in deep learning and scientific computing.

A100: It has enough performance to handle most AI and HPC tasks, while the cost is relatively controllable.

4090: It is suitable for gaming and multimedia processing, and can also handle lightweight AI and computing tasks.

2. Price considerations

The price of H100 is between $30,000 and $40,000, which is suitable for users with sufficient budget and extremely high performance requirements.

The price of A100 is about $15,000, which is a good balance between high performance and cost.

The 4090 only costs $1,600, which is extremely cost-effective for general users and small and medium-sized enterprises.

Through the detailed comparison and application analysis of the three graphics cards H100, A100 and 4090, we can see that the differences in performance, bandwidth, latency and price of different graphics cards determine their applicability in different application scenarios.

In the future, with the continuous advancement of technology, we can expect the advent of higher performance and lower power consumption graphics cards, which will further promote the development of AI, HPC and various computing tasks.

For developers and researchers, choosing the right graphics card will directly affect the efficiency and results of the project.

Under the premise of considering the budget, choosing the most suitable graphics card according to specific needs is a key step to achieve project success.