NVIDIA Advances Performance Records on AI Inference

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NVIDIA Extends Lead on MLPerf Benchmark with A100 Delivering as much as 237x Faster AI  Inference Than CPUs, Enabling Businesses to Move AI from Research to Production

NVIDIA as we speak introduced its AI computing platform has once more smashed efficiency information within the newest spherical of MLPerf, extending its lead on the business’s solely  unbiased benchmark measuring AI efficiency of {hardware}, software program and companies. 

NVIDIA gained each take a look at throughout all six software areas for knowledge heart and edge computing methods within the  second model of MLPerf Inference. The assessments broaden past the unique two for pc imaginative and prescient to  embody 4 masking the fastest-growing areas in AI: advice methods, pure language  understanding, speech recognition and medical imaging. 

NVIDIA A100

Organizations throughout a variety of industries are already tapping into the NVIDIA A100 GPU’s distinctive inference efficiency to take AI from their analysis teams into day by day operations. Financial  establishments are utilizing conversational AI to reply buyer questions quicker; retailers are utilizing AI to  hold cabinets stocked; and healthcare suppliers are utilizing AI to research hundreds of thousands of medical pictures to  extra precisely determine illness and assist save lives. 

“We’re at a tipping point as every industry seeks better ways to apply AI to offer new services and grow  their business,” mentioned Ian Buck, basic supervisor and vp of Accelerated Computing at NVIDIA.  “The work we’ve done to achieve these results on MLPerf gives companies a new level of AI  performance to improve our everyday lives.” 

The newest MLPerf outcomes come as NVIDIA’s footprint for AI inference has grown dramatically. Five years  in the past, solely a handful of main high-tech firms used GPUs for inference. Now, with NVIDIA’s AI  platform obtainable via each main cloud and knowledge heart infrastructure supplier, firms  representing a wide selection of industries are utilizing its AI inference platform to enhance their enterprise  operations and provide extra companies.  

Additionally, for the primary time, NVIDIA GPUs now provide extra AI inference capability within the public cloud  than CPUs. Total cloud AI inference compute capability on NVIDIA GPUs has been rising roughly 10x  each two years. 

NVIDIA Takes AI Inference to New Heights 

NVIDIA and its companions submitted their MLPerf 0.7 outcomes utilizing NVIDIA’s acceleration platform, which  contains NVIDIA knowledge heart GPUs, edge AI accelerators and NVIDIA optimized software program. 

NVIDIA A100, launched earlier this yr and that includes third-generation Tensor Cores and Multi Instance GPU know-how, elevated its lead on the ResNet-50 take a look at, beating CPU-only methods by 30x versus 6x within the final spherical. Additionally, A100 outperformed the newest obtainable CPUs by as much as 237x in  the newly added recommender take a look at for knowledge heart inference, in keeping with the MLPerf Inference 0.7  benchmarks. 

This means a single DGX A100 server can present the identical efficiency as 950 dual-socket CPU servers,  providing prospects excessive value effectivity when taking their AI recommender fashions from analysis to  manufacturing.  

The benchmarks additionally present that NVIDIA T4 Tensor Core GPU continues to be a strong inference platform  for mainstream enterprise, edge servers and cost-effective cloud cases. NVIDIA T4 GPUs beat CPUs  by as much as 28x in the identical assessments. In addition, the NVIDIA Jetson AGX Xavier is the efficiency chief  amongst SoC-based edge gadgets. 

Achieving these outcomes required a extremely optimized software program stack together with NVIDIA TensorRT inference  optimizer and NVIDIA Triton inference serving software program, each obtainable on NGC, NVIDIA’s software program  catalog.  

In addition to NVIDIA’s personal submissions, 11 NVIDIA companions submitted a complete of 1,029 outcomes utilizing  NVIDIA GPUs, representing over 85 p.c of the entire submissions within the knowledge heart and edge  classes. 

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