1 Based on BERT-Large 128 sequence length training, 64 replica count, 1k batch size. 1.4x faster = Nvidia DGX A100 20,086 samples/second throughput, SambaNova Systems DataScale SN10-8R 28,800 samples/second throughput

Based on MLperf results published as of 11/24/2020.  Inference throughput SambaNova Systems RDU 8632 samples per second and Nvidia A100 1251 samples per second, 8632 / 1251 = 6.90x. Inference latency SambaNova Systems RDU 0.116 samples per second and Nvidia A100 0.799 samples per second, 0.799 / 0.116 = 6.98879x. 

3 CosmicTagger batch size 64, image 1,280×2,048. SambaNova Systems Cardinal SN10 RDU accuracy = 0.902399368584156

4 Based on MLPerf results published as of 11/24/2020 comparing the MLPerf accuracy threshold of 80.25%, with Intel Xeon CPU and Nvidia A100 GPU showing 80.27% accuracy.  SambaNova Systems Cardinal SN10-8R shows 80.46% accuracy achievement.

Based on customer testimonial referenced in Argonne National Labrotory video of system installation and running custom customer model in under 45 minutes

Stay on Top of AI

Sign up for AI trends,
information and company news.

    To learn more about your rights and the processing of your data, please refer to our Privacy Policy available at https://sambanova.ai/privacy-policy/


    for signing up.
    We will keep you posted.