Best GPUs for AI & Machine/Deep Learning in 2024(Benchmarks)

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As artificial intelligence (AI) and machine learning (ML) continue to advance, the demand for powerful GPUs capable of handling complex computations has never been greater. Whether you’re training large-scale deep neural networks or deploying AI models for inference, choosing the right GPU is crucial. In 2024, several GPUs stand out as top choices for AI and deep learning workloads. Here’s a detailed look at the best options available.

1. NVIDIA H200: The Next Evolution in AI & Machine Learning

The NVIDIA H200, an advancement in the Hopper architecture, represents the ultimate in GPU technology for AI and machine learning. As the successor to the H100, the H200 pushes the boundaries further with enhanced performance and capabilities. When compared to the H100, the key difference comes in the massive increase in VRAM, with 141GB of HBM3e memory offering a substantial upgrade to the H100’s 80GB HBM3. The H200 is capable of 43% higher GPU memory bandwidth than the H100, with a peak of 4.8TB/s and 900GB/s of P2P bandwidth.

Key Features

• Performance Leap: The H200 delivers even greater AI performance with advanced tensor cores and up to 6 PFLOPS, surpassing the H100 and setting new standards in computational power.
• Memory Bandwidth: Utilizing the latest HBM3 memory, the H200 provides exceptional bandwidth, ideal for handling even larger datasets and more complex models than its predecessors.
• Efficiency: It maintains high energy efficiency, offering superior performance per watt, crucial for extensive AI data centers.
• Scalability: Enhanced NVLink support allows for even more seamless scaling with multiple GPUs, optimizing large-scale AI training.
• H200 (SXM) Performance: 1,979 TFLOPS FP16

Applications The H200 excels in:

• Deep Learning: Further reducing training times for massive neural networks.
• Natural Language Processing (NLP): Handling extensive text data more efficiently.
• Data Analytics: Processing complex queries on expansive datasets with ease.

Best For: Leading-edge AI and machine learning innovations.

2. NVIDIA H100: The New Standard for AI & Machine Learning

The NVIDIA H100, part of the NVIDIA Hopper architecture, represents the cutting edge of GPU technology, especially for AI and machine learning tasks. Designed to outperform its predecessors, the H100 sets new benchmarks in deep learning, data analytics, and scientific computing.

Key Features

• Massive Performance Leap: The H100 is engineered with advanced tensor cores that accelerate mixed-precision matrix calculations, crucial for AI and deep learning models. With up to 5 PFLOPS of AI performance, it significantly surpasses the A100 in raw computational power.
• Memory Bandwidth: Equipped with HBM3 memory, the H100 offers unprecedented memory bandwidth, making it ideal for handling large datasets and complex AI models. This ensures that even the most demanding neural networks can be processed without bottlenecks.
• Efficiency: Despite its immense power, the H100 is designed with energy efficiency in mind. It provides high performance per watt, making it a suitable choice for large-scale AI data centers where energy consumption is a critical concern.
• Scalability: The H100 supports NVLink, allowing multiple GPUs to work together seamlessly, further enhancing performance for large-scale AI training tasks.
• H100 (PCIe) Performance: 1,513 TFLOPS FP16
• H100 (SXM) Performance: 1,979 TFLOPS FP16

Applications

The H100 is tailored for high-performance computing environments where AI and machine learning are at the forefront. It’s particularly effective in:

• Deep Learning: Accelerating training times for large neural networks.
• Natural Language Processing (NLP): Processing vast amounts of text data efficiently.
• Data Analytics: Handling complex queries on massive datasets with ease

Best For: organizations pushing the boundaries of AI and machine learning.

3. NVIDIA A100: The AI Powerhouse

The NVIDIA A100 is the pinnacle of performance in AI and deep learning, designed specifically for data centers and professional applications. Built on the Ampere architecture, the A100 offers exceptional computational power with its advanced Tensor Cores and extensive memory capacity.

• Ampere Architecture: The A100’s Ampere architecture introduces significant improvements over previous generations, including enhanced Tensor Cores that accelerate deep learning computations, resulting in faster training and inference times.
• High Memory Capacity: With up to 80 GB of HBM2e memory, the A100 can process large-scale models and datasets without hitting memory limitations.
• Multi-Instance GPU (MIG): The A100’s MIG technology allows a single GPU to be partitioned into multiple smaller instances, each with dedicated compute resources, enabling efficient use in multi-tenant environments.
• Performance: 312 TFLOPS FP16

Best For: Large-scale AI research, enterprise-level deep learning tasks, and data center applications.

4. NVIDIA RTX A6000: Professional Performance

The NVIDIA RTX A6000 is a professional-grade GPU that strikes an excellent balance between performance and cost. Built on the same Ampere architecture as the A100, the A6000 is equipped with advanced Tensor Cores and a large memory capacity, making it suitable for complex AI projects.

• High Performance: With 48 GB of GDDR6 memory and a high number of CUDA and Tensor Cores, the RTX A6000 can handle large datasets and complex computations efficiently.
• Versatile AI Features: The A6000’s Tensor Cores accelerate AI tasks, while its support for ray tracing enhances visual simulations, making it a versatile choice for both AI development and other professional applications.
• RTX A6000 Performance: 309.7 TFLOPS FP16
• RTX A6000 ADA Performance: 1,457 TFLOPS FP16

Best For: Researchers, AI professionals, and organizations looking for a powerful GPU that offers great value.

5. NVIDIA RTX 4090: Consumer-Level Powerhouse

The NVIDIA GeForce RTX 4090 is the top choice for those looking for high-end performance without the need for a professional GPU. Though primarily designed for gaming, the RTX 4090 offers excellent capabilities for AI and deep learning tasks, thanks to its CUDA and Tensor Cores.

• Impressive CUDA Cores: With 16,384 CUDA cores, the RTX 4090 delivers substantial processing power, making it a strong contender for deep learning workloads.
• High Memory Bandwidth: The RTX 4090’s 1 TB/s memory bandwidth ensures rapid data transfer, crucial for handling AI tasks efficiently.
• Accessibility: As a consumer-grade GPU, the RTX 4090 is more accessible and affordable than professional GPUs like the A100 and A6000, making it ideal for smaller-scale projects or individual researchers.
• Performance: 82.58 TFLOPS FP16

Best For: Enthusiasts, hobbyists, and professionals on a budget who need a powerful yet affordable solution for AI and deep learning.

6. NVIDIA L40: Professional AI and Visualization Powerhouse

The NVIDIA L40 GPU is a professional-grade GPU engineered for the most demanding AI, machine learning, and visualization tasks. Leveraging its advanced architecture, the L40 offers unmatched power for data scientists, researchers, and designers needing a reliable solution for complex computational workloads.

• Exceptional CUDA Cores: With 18,176 CUDA cores, the L40 delivers extraordinary processing power, ensuring it can handle intensive AI and deep learning operations with ease.
• High Memory Bandwidth: Equipped with a 1.7 TB/s memory bandwidth, the L40 supports rapid data throughput, essential for efficient handling of large datasets and real-time visualization.
• Professional-Grade Reliability: Unlike consumer GPUs, the L40 is built with enterprise-grade reliability and is optimized for high-performance environments, making it a perfect fit for commercial AI development and deployment.

FP16 Tensor Core performance: 181.05 TFLOPS
FP16 Tensor Core performance with Sparsity: 362 TFLOPS

Best For: Professionals, research institutions, and organizations needing robust, high-performance GPU capabilities for advanced AI, machine learning, and 3D visualization projects.

7. NVIDIA V100: The Legacy Choice

Although the NVIDIA V100 has been on the market since 2017, it remains a powerful option for AI and deep learning tasks. The V100, built on the Volta architecture, offers strong performance with its Tensor Cores and NVLink support.

• Volta Architecture: The V100’s architecture includes Tensor Cores specifically designed to accelerate deep learning, making it a reliable choice for training and inference tasks.
• NVLink Support: NVLink allows multiple V100 GPUs to work together, providing scalable performance for more demanding AI applications.
• Memory Capacity: Available with up to 32 GB of HBM2 memory, the V100 can handle large datasets and models effectively.
• Performance: 125 TFLOPS FP16

Best For: Enterprises and research institutions that need proven, reliable deep learning performance, especially in multi-GPU configurations.

Deep Learning GPU Benchmarks 2023–2024

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Conclusion

Choosing the right GPU for AI and machine/deep learning depends largely on the specific needs of your projects. The H200 is Best for Leading-edge AI and machine learning innovations, Its unmatched performance, coupled with advanced features and scalability, positions it as a leader in the next generation of GPUs. For organizations pushing the boundaries of AI and machine learning, the NVIDIA H100 is the another go-to choice. The NVIDIA A100 is also a good choice for those requiring the highest performance for large-scale tasks, while the RTX A6000 offers a professional-grade solution at a more accessible price. For smaller projects or those on a budget, the RTX 4090 provides an excellent balance of performance and cost. The NVIDIA L40 is ideal for Professionals, research institutions, and organizations needing robust, high-performance GPU capabilities for advanced AI, machine learning, and 3D visualization projects.

In 2024, these GPUs represent the best options for powering AI and deep learning advancements, each catering to different needs and budgets. Whether you’re an individual researcher, a large enterprise, or somewhere in between, there’s a GPU on this list that will meet your demands.