6 Best GPUs for AI and Deep Learning in 2025

In 2025, AI and deep learning continue to revolutionize industries, demanding robust hardware capable of handling complex computations. Choosing the right GPU can dramatically influence your workflow, whether you’re training large language models or deploying AI at scale. Here, we compare six of the most powerful GPUs for AI and deep learning: RTX 4090, RTX 5090, RTX A6000, RTX 6000 Ada, Tesla A100, and Nvidia L40s.

Architecture: Ada Lovelace

Launch Date: Oct. 2022

Computing Capability: 8.9

CUDA Cores: 16,384

Tensor Cores: 512 4th Gen

VRAM: 24 GB GDDR6X

Memory Bandwidth: 1.01 TB/s

Single-Precision Performance: 82.6 TFLOPS

Half-Precision Performance: 165.2 TFLOPS

Tensor Core Performance: 330 TFLOPS (FP16), 660 TOPS (INT8)

The RTX 4090, primarily designed for gaming, has proven its capability for AI tasks, especially for small to medium-scale projects. With its Ada Lovelace architecture and 24 GB of VRAM, it’s a cost-effective option for developers experimenting with deep learning models. However, its consumer-oriented design lacks enterprise-grade features like ECC memory.

Architecture: Blackwell 2.0

Launch Date: Jan. 2025

Computing Capability: 10.0

CUDA Cores: 21,760

Tensor Cores: 680 5th Gen

VRAM: 32 GB GDDR7

Memory Bandwidth: 1.79 TB/s

Single-Precision Performance: 104.8 TFLOPS

Half-Precision Performance: 104.8 TFLOPS

Tensor Core Performance: 450 TFLOPS (FP16), 900 TOPS (INT8)

The highly anticipated RTX 5090 introduces the Blackwell 2.0 architecture, delivering a significant performance leap over its predecessor. With increased CUDA cores and faster GDDR7 memory, it’s ideal for more demanding AI workloads. While not yet widely adopted in enterprise environments, its price-to-performance ratio makes it a strong contender for researchers and developers.

Architecture: Ampere

Launch Date: Apr. 2021

Computing Capability: 8.6

CUDA Cores: 10,752

Tensor Cores: 336 3rd Gen

VRAM: 48 GB GDDR6

Memory Bandwidth: 768 GB/s

Single-Precision Performance: 38.7 TFLOPS

Half-Precision Performance: 77.4 TFLOPS

Tensor Core Performance: 312 TFLOPS (FP16)

The RTX A6000 is a workstation powerhouse. Its large 48 GB VRAM and ECC support make it perfect for training large models. Although its Ampere architecture is older compared to Ada and Blackwell, it remains a go-to choice for professionals requiring stability and reliability in production environments.

Architecture: Ada Lovelace

Launch Date: Dec. 2022

Computing Capability: 8.9

CUDA Cores: 18,176

Tensor Cores: 568 4th Gen

VRAM: 48 GB GDDR6 ECC

Memory Bandwidth: 960 GB/s

Single-Precision Performance: 91.1 TFLOPS

Half-Precision Performance: 91.1 TFLOPS

Tensor Core Performance: 1457.0 FP8 TFLOPS

The RTX 6000 Ada combines the strengths of Ada Lovelace architecture with enterprise-grade features, including ECC memory. It is designed for cutting-edge AI tasks, such as fine-tuning foundation models and large-scale inference. Its efficient power consumption and exceptional performance make it a preferred choice for high-end professional use.

Architecture: Ampere

Launch Date: May. 2020

Computing Capability: 8.0

CUDA Cores: 6,912

Tensor Cores: 432 3rd Gen

VRAM: 40/80 GB HBM2e

Memory Bandwidth: 1,935GB/s 2,039 GB/s

Single-Precision Performance: 19.5 TFLOPS

Double-Precision Performance: 9.7 TFLOPS

Tensor Core Performance: FP64 19.5 TFLOPS, Float 32 156 TFLOPS, BFLOAT16 312 TFLOPS, FP16 312 TFLOPS, INT8 624 TOPS

The Tesla A100 is built for data centers and excels in large-scale AI training and HPC tasks. Its Multi-Instance GPU (MIG) feature allows partitioning into multiple smaller GPUs, making it highly versatile. The A100’s HBM2e memory ensures unmatched memory bandwidth, making it ideal for training massive AI models like GPT variants.

Architecture: Ada Lovelace

Launch Date: Oct. 2022

Computing Capability: 8.9

CUDA Cores: 18,176

Tensor Cores: 568 4th Gen

VRAM: 48 GB GDDR6 ECC

Memory Bandwidth: 864GB/s

Single-Precision Performance: 91.6 TFLOPS

Half-Precision Performance: 91.6 TFLOPS

Tensor Core Performance: INT4 TOPS 733, INT8 TOPS 733, FP8 733 TFLOPS, FP16 362.05 TFLOPS, BFLOAT16 TFLOPS 362.05, TF32 TFLOPS 183

The Nvidia L40s, an enterprise-grade GPU, is designed for versatility across AI, graphics, and rendering tasks. Its Ada Lovelace architecture and ECC memory make it a robust choice for AI training and deployment. With a balance of performance and efficiency, the L40s is suited for cloud deployments and hybrid environments.

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 >>

Choosing the right GPU for AI and deep learning depends on workload, budget, and scalability needs. For entry-level or small-scale projects, the RTX 4090 is an affordable option with strong performance. Researchers and developers working on advanced tasks can benefit from the RTX 5090, which offers cutting-edge features and excellent performance for demanding models. Enterprise-grade GPUs like the RTX A6000 and RTX 6000 Ada are ideal for production environments, providing large VRAM and ECC memory for stability. The Tesla A100 excels in large-scale training and high-performance computing with its multi-instance GPU support and exceptional memory bandwidth. The Nvidia L40s balances AI performance with versatility for hybrid enterprise workloads.

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.