Shattering AI Performance Records
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NVIDIA's Volta Tensor Core GPU has set new AI performance records, achieving significant improvements in deep learning, particularly with the ResNet-50 model. The V100 GPU demonstrates remarkable efficiencies, achieving 1,075 images per second in training—four times faster than its predecessor—and a single DGX-1 server can manage nearly 8,000 images per second. Comparatively, a single AWS P3 cloud instance with V100 GPUs can train the same model in under three hours, significantly outpacing competing technologies like Google's TPU.
![FASTEST SINGLE NODE
A single DGX-1 server powered by eight
Tensor Core V100s achieves
7,850 images/second, almost 2X the 4,200
images/second from a year ago on the
same system.
“I feel like it’s important to note that these performance improvements [by NVIDIA] are more important than they immediately appear, because while
these gains dramatically impact today’s workloads, they’re effectively preempting even more complex workloads of the future.”
- Rob Williams, TechGage](https://image.slidesharecdn.com/voltaperfclaimpptresnet50v3an-180509163136/85/Shattering-AI-Performance-Records-6-320.jpg)
Shattering AI Performance Records
- 1. SHATTERING AI PERFORMANCE RECORDS NVIDIA Volta Tensor Core GPU Achieves New AI Performance Milestones
- 2. GPU-POWERED DEEP LEARNING IS TRANSFORMING EVERY INDUSTRY, SOLVING CHALLENGES ONCE THOUGHT IMPOSSIBLE…
- 3. THE IDEAL AI COMPUTING PLATFORM NEEDS TO PROVIDE IMPROVED PERFORMANCE, SCALABILITY AND PROGRAMMABILITY TO ADDRESS THE DIVERSITY OF MODEL ARCHITECTURES.
- 4. NVIDIA’S VOLTA TENSOR CORE GPU ACHIEVED RECORD-SHATTERING RESNET-50 PERFORMANCE FOR A SINGLE CHIP, SINGLE NODE, AND SINGLE CLOUD INSTANCE.
- 5. FASTEST SINGLE CHIP A single V100 Tensor Core GPU achieves 1,075 images/second when training ResNet-50, a 4X performance increase compared to the previous generation Pascal GPU. “New figures from NVIDIA illustrate the contribution hardware improvements can make to progress in machine learning: the AlexNet model that won ImageNet in 2012 took six days to train, can now be done in 18 minutes — a 500x speedup.” - Tom Simonite, WIRED
- 6. FASTEST SINGLE NODE A single DGX-1 server powered by eight Tensor Core V100s achieves 7,850 images/second, almost 2X the 4,200 images/second from a year ago on the same system. “I feel like it’s important to note that these performance improvements [by NVIDIA] are more important than they immediately appear, because while these gains dramatically impact today’s workloads, they’re effectively preempting even more complex workloads of the future.” - Rob Williams, TechGage
- 7. FASTEST SINGLE CLOUD INSTANCE A single AWS P3 cloud instance powered by eight Tensor Core V100 GPUs can train ResNet-50 in less than three hours, 3X faster than a TPU instance. “4 #TPU chips in a ‘Cloud TPU’ deliver 180 teraFLOPS of performance; by comparison, four V100 chips deliver 500 teraFLOPS. #NVIDIAwins.” - Karl Freund, Moor Insights
- 8. NVIDIA TENSOR CORE GPU ARCHITECTURE ALLOWS US TO SIMULTANEOUSLY PROVIDE GREATER PERFORMANCE THAN SINGLE-FUNCTION ASICS, YET BE PROGRAMMABLE FOR DIVERSE WORKLOADS.
- 9. EACH TESLA V100 TENSOR CORE GPU DELIVERS 125 TERAFLOPS OF PERFORMANCE FOR DEEP LEARNING COMPARED TO 45 TERAFLOPS BY A GOOGLE TPU CHIP. 4 TPU CHIPS IN A ‘CLOUD TPU’ V2 DELIVER 180 TERAFLOPS OF PERFORMANCE. BY COMPARISON, 4 NVIDIA V100 CHIPS DELIVER 500 TERAFLOPS OF PERFORMANCE.
- 10. EXPLORE THE PERFORMANCE IMPROVEMENTS HERE