# DeepSeek

NVIDIA wired Transformer Engine's MoE optimizations into the JAX training stack so every token reaches its selected expert, with no dropping and no padding. An unoptimized baseline training DeepSeek-V3 on GB300 reached only 103 TFLOPS/GPU, with inter-GPU communication consuming 84% of accumulated kernel time; grouped GEMM, MXFP8 grouped quantization and fused expert-parallel dispatch and combine raised that to 1,068 TFLOPS/GPU, about a tenfold end-to-end throughput gain, and the stack sustained 97% efficiency at 1,024 GPUs.

NVIDIA wired Transformer Engine's MoE optimizations into the JAX training stack so every token reaches its selected expert, with no dropping and no padding. An unoptimized baseline training DeepSeek-V3 on GB300 reached only 103 TFLOPS/GPU, with inter-GPU communication consuming 84% of accumulated kernel time; grouped GEMM, MXFP8 grouped quantization and fused expert-parallel dispatch and combine raised that to 1,068 TFLOPS/GPU, about a tenfold end-to-end throughput gain, and the stack sustained 97% efficiency at 1,024 GPUs.

In this cross-sectional comparative study, 49 second-year dental students completed a 45-item multiple-choice final examination in prosthodontic technology under standardized conditions, the same items were submitted to three AI systems (ChatGPT, Gemini, and DeepSeek), and analysis using ANOVA, Cronbach Alpha, and Tukey HSD showed that DeepSeek achieved perfect scores on high-difficulty questions while ChatGPT showed adaptive improvement across attempts, both significantly outperforming the student cohort, whereas Gemini demonstrated lower and consistent accuracy, with significant differences between models (P=0.036, eta-squared=0.891), leading the authors to argue that unsupervised online summative examinations require critical reevaluation of assessment integrity.

In this cross-sectional comparative study, 49 second-year dental students completed a 45-item multiple-choice final examination in prosthodontic technology under standardized conditions, the same items were submitted to three AI systems (ChatGPT, Gemini, and DeepSeek), and analysis using ANOVA, Cronbach Alpha, and Tukey HSD showed that DeepSeek achieved perfect scores on high-difficulty questions while ChatGPT showed adaptive improvement across attempts, both significantly outperforming the student cohort, whereas Gemini demonstrated lower and consistent accuracy, with significant differences between models (P=0.036, eta-squared=0.891), leading the authors to argue that unsupervised online summative examinations require critical reevaluation of assessment integrity.