5 Best GPUs for AI and Deep Learning in 2024

An In-Depth Comparison of NVIDIA A100, RTX A6000, RTX 4090, NVIDIA A40, Tesla V100

Top 1. NVIDIA A100

The NVIDIA A100 is an excellent GPU for deep learning. It is specifically designed for data center and professional applications, including deep learning tasks. Here are some reasons why the A100 is considered a powerful choice for deep learning:

- 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.

These features make the NVIDIA A100 an exceptional choice for deep learning tasks. It provides high performance, advanced AI capabilities, large memory capacity, and efficient utilization of computational resources, all of which are crucial for training and running complex deep neural networks.

Top 2. NVIDIA RTX A6000

The NVIDIA RTX A6000 is a powerful GPU that is well-suited for deep learning applications. The RTX A6000 is based on the Ampere architecture and is part of NVIDIA's professional GPU lineup. It offers excellent performance, advanced AI features, and a large memory capacity, making it suitable for training and running deep neural networks. Here are some key features of the RTX A6000 that make it a good choice for deep learning:

- Ampere Architecture: The RTX A6000 is built on NVIDIA's Ampere architecture, which delivers significant performance improvements over previous generations. It features advanced Tensor Cores for AI acceleration, enhanced ray tracing capabilities, and increased memory bandwidth.

- High Performance: The RTX A6000 offers a high number of CUDA cores, Tensor Cores, and ray-tracing cores, resulting in fast and efficient deep learning performance. It can handle large-scale deep learning models and complex computations required for training neural networks.

- Large Memory Capacity: The RTX A6000 comes with 48 GB of GDDR6 memory, providing ample memory space for storing and processing large datasets. Having a large memory capacity is beneficial for training deep learning models that require a significant amount of memory.

- AI Features: The RTX A6000 includes dedicated Tensor Cores, which accelerate AI computations and enable mixed-precision training. These Tensor Cores can significantly speed up deep learning workloads by performing operations like matrix multiplications at an accelerated rate.

While the RTX A6000 is primarily designed for professional applications, it can certainly be used effectively for deep learning tasks. Its high performance, memory capacity, and AI-specific features make it a powerful option for training and running deep neural networks.

Top 3. NVIDIA RTX 4090

The NVIDIA GeForce RTX 4090 is a powerful consumer-grade graphics card that can be used for deep learning, but it is not as well-suited for this task as professional GPUs like the Nvidia A100 or RTX A6000.

Advantages of the RTX 4090 for deep learning:

- High number of CUDA cores: The RTX 4090 has 16384 CUDA cores, which are the processing units responsible for performing deep learning calculations.

- High memory bandwidth: The RTX 4090 has a memory bandwidth of 1 TB/s, which allows it to transfer data to and from memory quickly.

- Large memory capacity: The RTX 4090 has 24GB of GDDR6X memory, which is sufficient for training small to medium-sized deep learning models.

- Support for CUDA and cuDNN: The RTX 4090 is fully supported by Nvidia's CUDA and cuDNN libraries, which are essential for developing and optimizing deep learning models.


Disadvantages of the RTX 4090 for deep learning:

- Lower number of tensor cores: The RTX 4090 has only 128 tensor cores, which are specialized hardware units designed to accelerate matrix operations common in deep learning algorithms. Professional GPUs like the A100 and A6000 have significantly more tensor cores, providing a performance advantage for deep learning tasks.

- Lower memory capacity: The RTX 4090's 24GB of memory is sufficient for small to medium-sized models, but it may be limiting for training large models or working with large datasets.

- Lack of NVLink support: The RTX 4090 does not support NVLink, which is a high-speed interconnect technology that allows multiple GPUs to be connected together to scale performance. This makes the RTX 4090 less suitable for building large-scale deep learning clusters.

Overall, the RTX 4090 is a capable GPU for deep learning, but it is not as well-suited for this task as professional GPUs like the Nvidia A100 or RTX A6000. If you are serious about deep learning and require the highest possible performance, a professional GPU is a better choice. However, if you are on a budget or only need to train small to medium-sized models, the RTX 4090 can be a good option.

Top 4. NVIDIA A40

The NVIDIA A40 is a capable GPU for deep learning tasks. While it is primarily designed for data center and professional applications, it can also be utilized effectively for deep learning workloads. Here are some reasons why the A40 is suitable for deep learning:

- Ampere Architecture: The A40 is based on NVIDIA's Ampere architecture, which brings significant performance improvements and AI-specific features. It includes Tensor Cores for accelerated deep learning computations, resulting in faster training and inference times.

- High Performance: The A40 offers a high number of CUDA cores and Tensor Cores, providing substantial compute power for deep learning tasks. It can handle large-scale models and complex computations required for training deep neural networks.

- Memory Capacity: The A40 comes with 48 GB of GDDR6 memory, providing ample space for storing and processing large datasets. Sufficient memory capacity is crucial for training deep learning models that require extensive memory access.

- AI and Deep Learning Optimization: The A40 benefits from NVIDIA's deep learning software stack, including CUDA, cuDNN, and TensorRT. These software libraries are optimized for deep learning workloads, ensuring efficient utilization of the GPU's resources and delivering high performance.

- Compatibility and Support: The A40 is compatible with popular deep learning frameworks, such as TensorFlow, PyTorch, and MXNet. It is backed by NVIDIA's extensive ecosystem and developer support, making it easier to integrate into existing deep learning workflows.

While the A40 may not offer the same level of performance as high-end GPUs like the A100, it still provides substantial compute power and AI-specific features that make it a suitable choice for deep learning tasks. It offers a balance between performance and affordability, making it a practical option for organizations and researchers working on deep learning projects.

Top 5. NVIDIA V100

The NVIDIA V100 is an excellent GPU for deep learning. It is designed specifically for high-performance computing and AI workloads, making it well-suited for deep learning tasks. Here are some reasons why the V100 is considered a powerful choice for deep learning:

- Volta Architecture: The V100 is based on NVIDIA's Volta architecture, which offers significant advancements in performance and AI-specific features. It includes Tensor Cores, which accelerate deep learning computations, resulting in faster training and inference times.

- High Performance: The V100 is a high-performance GPU with a large number of CUDA cores, Tensor Cores, and high memory bandwidth. It can handle complex deep learning models and large datasets, delivering exceptional performance for training and inference workloads.

- Memory Capacity: The V100 offers a generous memory capacity of up to 32 GB with HBM2 memory technology, providing sufficient space for storing and processing large datasets. This is crucial for deep learning tasks that require extensive memory access.

- Mixed-Precision Training: The V100 supports mixed-precision training, allowing for a combination of lower-precision (such as FP16) and higher-precision (such as FP32) calculations. This enables faster training while maintaining acceptable levels of accuracy.

- NVLink Interconnect: The V100 features NVLink, a high-speed interconnect technology that allows multiple GPUs to work together in a single system. This enables scalable multi-GPU configurations for even higher performance in deep learning applications.

The NVIDIA V100 has been widely adopted in data centers and high-performance computing environments for deep learning tasks. Its powerful architecture, high performance, and AI-specific features make it a reliable choice for training and running complex deep neural networks. It is worth noting that the V100 might be more common in professional and enterprise settings due to its price point, but it remains a highly capable GPU for deep learning.

Technical Specifications

NVIDIA A100RTX A6000RTX 4090NVIDIA A40NVIDIA V100
ArchitectureAmpereAmpereAda LovelaceAmpereVolta
Launch20202020202220202017
CUDA Cores6,91210,75216,38410,7525,120
Tensor Cores432, Gen 3336, Gen 3512, Gen 4336, Gen 3640, Gen 1
Boost Clock (GHz)1.411.412.231.101.53
FP16 TFLOPs7838.782.63728
FP32 TFLOPs19.538.782.63714
FP64 TFLOPs9.71.21.30.67
Pixel Rate225.6 GPixel/s201.6 GPixel/s483.8 GPixel/s194.9 GPixel/s176.6 GPixel/s
Texture Rate609.1 GTexel/s604.8 GTexel/s1290 GTexel/s584.6 GTexel/s441.6 GTexel/s
Memory40/80GB HBM2e48GB GDDR624GB GDDR6X48GB GDDR616/32GB HBM2
Memory Bandwidth1.6 TB/s768 GB/s1 TB/s672 GB/s900 GB/s
InterconnectNVLinkNVLinkN/ANVLinkNVLink
TDP250W/400W250W450W300W250W
Transistors54.2B54.2B76B54.2B21.1B
Manufacturing7nm7nm4nm7nm12nm

Deep Learning GPU Benchmarks 2023–2024

Resnet50 (FP16)
resnet50 fp16 benchmarks
Resnet50 (FP32)
resnet50 fp32 benchmarks

Best GPUs for deep learning, AI development, compute in 2023–2024. Recommended GPU & hardware for AI training, inference (LLMs, generative AI). GPU training, inference benchmarks using PyTorch, TensorFlow for computer vision (CV), NLP, text-to-speech, etc. Click here to learn more >>

Conclusion

The most suitable graphics card for deep learning depends on the specific requirements of the task. For demanding tasks requiring high performance, the Nvidia A100 is the best choice. For medium-scale tasks, the RTX A6000 offers a good balance of performance and cost. The RTX 4090 is a suitable option for smaller-scale tasks or hobbyists. The Nvidia V100 is a cost-effective option for moderate requirements, while the Nvidia A40 is ideal for entry-level deep learning tasks.

Summer Sale

Advanced GPU - A4000

167.2/mo
Save 40% (Was $279.00)
1mo3mo12mo24mo
Order Now
  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A4000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 6144
  • Tensor Cores: 192
  • GPU Memory: 16GB GDDR6
  • FP32 Performance: 19.2 TFLOPS
  • Good Choice for Android Emulators, 3D Rendering, Video Editing, AI/Deep Learning, Data Science, etc

Advanced GPU - A5000

269.00/mo
1mo3mo12mo24mo
Order Now
  • 128GB RAM
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 2
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
  • High-Powered for Android Emulators, 3D Rendering, Video Editing, AI/Deep Learning, etc.

Enterprise GPU - RTX A6000

409.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Enterprise GPU - RTX 4090

409.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 1
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Enterprise GPU - A40

439.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS
  • Higher-Powered for 3D Rendering, Video Editing, AI/Deep Learning, etc.

Advanced GPU - V100

229.00/mo
1mo3mo12mo24mo
Order Now
  • 128GB RAM
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 1
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
New Arrival

Enterprise GPU - A100

639.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • Max GPUs: 1
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
New Arrival

Multi-GPU - 3xV100

469.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Nvidia V100
  • Microarchitecture: Volta
  • Max GPUs: 3
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS
New Arrival

Multi-GPU - 3xRTX A5000

539.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A5000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 8192
  • Tensor Cores: 256
  • GPU Memory: 24GB GDDR6
  • FP32 Performance: 27.8 TFLOPS
New Arrival

Multi-GPU - 3xRTX A6000

899.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 3 x Quadro RTX A6000
  • Microarchitecture: Ampere
  • Max GPUs: 3
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS

Multi-GPU - 2xRTX 4090

729.00/mo
1mo3mo12mo24mo
Order Now
  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: 2 x GeForce RTX 4090
  • Microarchitecture: Ada Lovelace
  • Max GPUs: 2
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
Let us get back to you

If you can't find a suitable GPU Plan, or have a need to customize a GPU server, or have ideas for cooperation, please leave me a message. We will reach you back within 36 hours.

Email *
Name
Company
Message *
I agree to be contacted as per Database Mart privacy policy.