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RTX 3090 Benchmarks for Deep Learning – NVIDIA RTX 3090 vs 2080 Ti vs TITAN RTX vs RTX 6000/8000
October 18, 2020
19 min read
blog-3090-benchmarks.jpg

NVIDIA RTX 3090 Benchmarks for TensorFlow

For this blog article, we conducted deep learning performance benchmarks for TensorFlow on NVIDIA GeForce RTX 3090 GPUs.

Our Deep Learning workstation was fitted with two RTX 3090 GPUs and we ran the standard “tf_cnn_benchmarks.py” benchmark script found in the official TensorFlow github. We tested on the the following networks: ResNet50, ResNet152, Inception v3, Inception v4. Furthermore, we ran the same tests using 1, 2, and 4 GPU configurations (for the 2x RTX 3090 vs 4x 2080Ti section). Determined batch size was the largest that could fit into available GPU memory.

Key Points and Observations

  • The NVIDIA RTX 3090 outperformed all GPUs (Images/sec) across all models.
  • A system with 2x RTX 3090 > 4x RTX 2080 Ti.
  • For deep learning, the RTX 3090 is the best value GPU on the market and substantially reduces the cost of an AI workstation.

Interested in getting faster results?
Learn more about Exxact deep learning workstations starting at $3,700

RTX 3090 ResNet 50 TensorFlow Benchmark

Slide1.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti522.52959.78128
RTX 6000637.561248.54512
RTX 8000604.761184.521024
TITAN RTX646.131287.01512
RTX 30901139.152153.53512

RTX 3090 ResNet 152 TensorFlow Benchmark

Slide2.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti209.27348.864
RTX 6000281.94519.76256
RTX 8000285.85529.05512
TITAN RTX284.87530.86256
RTX 3090457.45857.14256

RTX 3090 Inception V3 TensorFlow Benchmark

Slide3.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti310.32569.24128
RTX 6000391.08737.77256
RTX 8000391.3754.94512
TITAN RTX397.09784.24256
RTX 3090697.981296.86256

RTX 3090 Inception V4 TensorFlow Benchmark

Slide4.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti150.59247.1664
RTX 6000203.9392.14256
RTX 8000203.67384.29512
TITAN RTX207.98399.16256
RTX 3090360679.61256

2x NVIDIA RTX 3090 Vs 4x RTX 2080 Ti – What config is Better?

Slide5.PNG

1x GPU2x GPU4x GPUbatch size
RTX 2080 Ti522.52959.781836.61128
RTX 30901139.152153.53N/A512

TF CNN Benchmark Parameters

DescriptionType
Number of Batches100
Number of Epochs0.01
Data FormatNCHW
OptimizerMomentum
Variablesparameter_server

More About NVIDIA RTX 3090

The NVIDIA RTX 3090 has 24GB GDDR6X memory and is built with enhanced RT Cores and Tensor Cores, new streaming multiprocessors, and super fast G6X memory for an amazing performance boost.

Compared with RTX 2080 Ti’s 4352 CUDA Cores, the RTX 3090 more than doubles it with 10496 CUDA Cores. CUDA Cores are the GPU equivalent of CPU cores, and are optimized for running a large number of calculations simultaneously (parallel processing). More CUDA Cores generally mean better performance and faster graphics-intensive processing.

Have any questions about NVIDIA GPUs or AI workstations and servers?
Contact Exxact Today

Free Resources

Browse our whitepapers, e-books, case studies, and reference architecture.

Explore
blog-3090-benchmarks.jpg
Benchmarks
RTX 3090 Benchmarks for Deep Learning – NVIDIA RTX 3090 vs 2080 Ti vs TITAN RTX vs RTX 6000/8000
October 18, 2020 19 min read

NVIDIA RTX 3090 Benchmarks for TensorFlow

For this blog article, we conducted deep learning performance benchmarks for TensorFlow on NVIDIA GeForce RTX 3090 GPUs.

Our Deep Learning workstation was fitted with two RTX 3090 GPUs and we ran the standard “tf_cnn_benchmarks.py” benchmark script found in the official TensorFlow github. We tested on the the following networks: ResNet50, ResNet152, Inception v3, Inception v4. Furthermore, we ran the same tests using 1, 2, and 4 GPU configurations (for the 2x RTX 3090 vs 4x 2080Ti section). Determined batch size was the largest that could fit into available GPU memory.

Key Points and Observations

  • The NVIDIA RTX 3090 outperformed all GPUs (Images/sec) across all models.
  • A system with 2x RTX 3090 > 4x RTX 2080 Ti.
  • For deep learning, the RTX 3090 is the best value GPU on the market and substantially reduces the cost of an AI workstation.

Interested in getting faster results?
Learn more about Exxact deep learning workstations starting at $3,700

RTX 3090 ResNet 50 TensorFlow Benchmark

Slide1.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti522.52959.78128
RTX 6000637.561248.54512
RTX 8000604.761184.521024
TITAN RTX646.131287.01512
RTX 30901139.152153.53512

RTX 3090 ResNet 152 TensorFlow Benchmark

Slide2.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti209.27348.864
RTX 6000281.94519.76256
RTX 8000285.85529.05512
TITAN RTX284.87530.86256
RTX 3090457.45857.14256

RTX 3090 Inception V3 TensorFlow Benchmark

Slide3.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti310.32569.24128
RTX 6000391.08737.77256
RTX 8000391.3754.94512
TITAN RTX397.09784.24256
RTX 3090697.981296.86256

RTX 3090 Inception V4 TensorFlow Benchmark

Slide4.PNG

1x GPU2x GPUbatch size
RTX 2080 Ti150.59247.1664
RTX 6000203.9392.14256
RTX 8000203.67384.29512
TITAN RTX207.98399.16256
RTX 3090360679.61256

2x NVIDIA RTX 3090 Vs 4x RTX 2080 Ti – What config is Better?

Slide5.PNG

1x GPU2x GPU4x GPUbatch size
RTX 2080 Ti522.52959.781836.61128
RTX 30901139.152153.53N/A512

TF CNN Benchmark Parameters

DescriptionType
Number of Batches100
Number of Epochs0.01
Data FormatNCHW
OptimizerMomentum
Variablesparameter_server

More About NVIDIA RTX 3090

The NVIDIA RTX 3090 has 24GB GDDR6X memory and is built with enhanced RT Cores and Tensor Cores, new streaming multiprocessors, and super fast G6X memory for an amazing performance boost.

Compared with RTX 2080 Ti’s 4352 CUDA Cores, the RTX 3090 more than doubles it with 10496 CUDA Cores. CUDA Cores are the GPU equivalent of CPU cores, and are optimized for running a large number of calculations simultaneously (parallel processing). More CUDA Cores generally mean better performance and faster graphics-intensive processing.

Have any questions about NVIDIA GPUs or AI workstations and servers?
Contact Exxact Today

Free Resources

Browse our whitepapers, e-books, case studies, and reference architecture.

Explore