6 Best GPUs for AI and Deep Learning in 2025

An In-Depth Comparison of RTX 4090, RTX 5090, RTX A6000, RTX 6000 Ada, Tesla A100, and Nvidia L40s.

Introduction

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.

1. NVIDIA RTX 4090

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.

2. NVIDIA RTX 5090

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.

3. NVIDIA RTX A6000

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.

4. NVIDIA RTX 6000 Ada

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.

5. NVIDIA Tesla A100

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.

6. NVIDIA L40s

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.

Technical Specifications

NVIDIA A100RTX A6000RTX 4090RTX 5090RTX 6000 AdaNVIDIA L40s
ArchitectureAmpereAmpereAda LovelaceBlackwell 2.0Ada LovelaceAda Lovelace
LaunchMay. 2020Apr. 2021Oct. 2022Jan. 2025Dec. 2022Oct. 2022
CUDA Cores6,91210,75216,38421,76018,17618,176
Tensor Cores432, Gen 3336, Gen 3512, Gen 4680 5th Gen568 4th Gen568 4th Gen
Boost Clock (GHz)1.411.412.232.412.512.52
FP16 TFLOPs7838.782.6104.891.191.6
FP32 TFLOPs19.538.782.6104.891.191.6
FP64 TFLOPs9.71.21.31.61.41.4
Computing Capability8.08.68.910.08.98.9
Pixel Rate225.6 GPixel/s201.6 GPixel/s483.8 GPixel/s462.1 GPixel/s481.0 GPixel/s483.8 GPixel/s
Texture Rate609.1 GTexel/s604.8 GTexel/s1,290 GTexel/s1,637 GTexel/s1,423 GTexel/s1,431 GTexel/s
Memory40/80GB HBM2e48GB GDDR624GB GDDR6X32GB GDDR748 GB GDDR6 ECC48 GB GDDR6 ECC
Memory Bandwidth1.6 TB/s768 GB/s1 TB/s1.79 TB/s960 GB/s864GB/s
InterconnectNVLinkNVLinkN/ANVLinkN/AN/A
TDP250W/400W250W450W300W300W350W
Transistors54.2B54.2B76B54.2B76.3B76.3B
Manufacturing7nm7nm4nm7nm5nm4nm

Deep Learning GPU Benchmarks 2024–2025

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

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.

GPU Server Recommendation

Enterprise GPU Dedicated Server - RTX A6000

409.00/mo
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  • 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
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 38.71 TFLOPS
  • Optimally running AI, deep learning, data visualization, HPC, etc.
AI Servers, Smarter Deals!

Enterprise GPU Dedicated Server - RTX 4090

302.00/mo
44% Off Recurring (Was $549.00)
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  • 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
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS
  • Perfect for 3D rendering/modeling , CAD/ professional design, video editing, gaming, HPC, AI/deep learning.

Multi-GPU Dedicated Server- 2xRTX 4090

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

Multi-GPU Dedicated Server- 4xRTX 5090

999.00/mo
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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 4 x GeForce RTX 5090
  • Microarchitecture: Ada Lovelace
  • CUDA Cores: 20,480
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS

Enterprise GPU Dedicated Server - A40

439.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A40
  • Microarchitecture: Ampere
  • CUDA Cores: 10,752
  • Tensor Cores: 336
  • GPU Memory: 48GB GDDR6
  • FP32 Performance: 37.48 TFLOPS
  • Ideal for hosting AI image generator, deep learning, HPC, 3D Rendering, VR/AR etc.
AI Servers, Smarter Deals!

Enterprise GPU Dedicated Server - A100

469.00/mo
41% OFF Recurring (Was $799.00)
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS
  • Good alternativeto A800, H100, H800, L40. Support FP64 precision computation, large-scale inference/AI training/ML.etc

Multi-GPU Dedicated Server - 4xA100

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  • 512GB RAM
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • GPU: 4 x Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS
New Arrival

Enterprise GPU Dedicated Server - A100(80GB)

1559.00/mo
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  • 256GB RAM
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • GPU: Nvidia A100
  • Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5 TFLOPS
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