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.
NVIDIA A100 | RTX A6000 | RTX 4090 | RTX 5090 | RTX 6000 Ada | NVIDIA L40s | |
---|---|---|---|---|---|---|
Architecture | Ampere | Ampere | Ada Lovelace | Blackwell 2.0 | Ada Lovelace | Ada Lovelace |
Launch | May. 2020 | Apr. 2021 | Oct. 2022 | Jan. 2025 | Dec. 2022 | Oct. 2022 |
CUDA Cores | 6,912 | 10,752 | 16,384 | 21,760 | 18,176 | 18,176 |
Tensor Cores | 432, Gen 3 | 336, Gen 3 | 512, Gen 4 | 680 5th Gen | 568 4th Gen | 568 4th Gen |
Boost Clock (GHz) | 1.41 | 1.41 | 2.23 | 2.41 | 2.51 | 2.52 |
FP16 TFLOPs | 78 | 38.7 | 82.6 | 104.8 | 91.1 | 91.6 |
FP32 TFLOPs | 19.5 | 38.7 | 82.6 | 104.8 | 91.1 | 91.6 |
FP64 TFLOPs | 9.7 | 1.2 | 1.3 | 1.6 | 1.4 | 1.4 |
Computing Capability | 8.0 | 8.6 | 8.9 | 10.0 | 8.9 | 8.9 |
Pixel Rate | 225.6 GPixel/s | 201.6 GPixel/s | 483.8 GPixel/s | 462.1 GPixel/s | 481.0 GPixel/s | 483.8 GPixel/s |
Texture Rate | 609.1 GTexel/s | 604.8 GTexel/s | 1,290 GTexel/s | 1,637 GTexel/s | 1,423 GTexel/s | 1,431 GTexel/s |
Memory | 40/80GB HBM2e | 48GB GDDR6 | 24GB GDDR6X | 32GB GDDR7 | 48 GB GDDR6 ECC | 48 GB GDDR6 ECC |
Memory Bandwidth | 1.6 TB/s | 768 GB/s | 1 TB/s | 1.79 TB/s | 960 GB/s | 864GB/s |
Interconnect | NVLink | NVLink | N/A | NVLink | N/A | N/A |
TDP | 250W/400W | 250W | 450W | 300W | 300W | 350W |
Transistors | 54.2B | 54.2B | 76B | 54.2B | 76.3B | 76.3B |
Manufacturing | 7nm | 7nm | 4nm | 7nm | 5nm | 4nm |
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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.
Enterprise GPU Dedicated Server - RTX A6000
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Multi-GPU Dedicated Server- 2xRTX 4090
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Enterprise GPU Dedicated Server - A100
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Enterprise GPU Dedicated Server - A100(80GB)
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