costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2. Contents 1 Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.3 Training and Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Alignment 16 4.1 Supervised Fine-Tuning Multi-Head Attention (MHA) (Vaswani et al., 2017) poses a significant obstacle to the inference efficiency of LLMs. Various approaches have been explored to address this issue, including Grouped-Query Attention

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Set Convolution, Max Pool etc. ˃ Any Network, Any Image Size ˃ High Frequency & High Compute Efficiency ˃ Supported on U200 – 3 Instances U250 – 4 Instances Amazon F1 ˃ ~1536 DSPs @ 700MHz Execution WB WR SCHEDULER CTRL SIGNALS MISC CALC AVG POOL MAX POOL ROI POOL ELEMENT WISE ... Efficiency > 50% for mainstream neural networks >> 4© Copyright 2018 Xilinx Inference Flow >> 5 MxNet

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 worked together to fine-tune a smaller GPT model that was able to deliver connections. The result: end-to-end automation, freeing teams from repetitive tasks and boosting efficiency across the enterprise. 22 AI in the EnterpriseThe trusted AI enterprise platform Security and

GPU /NiiOS ! 驱动万物智能 8000% 7000% 6000% 5000% 4000% 3000% 2000% 1000% 0o0% GEMM Hardware Efficiency @ Intel Apollo Lake GPU 60.39% 512,512,512 国OpenVINO 国TVM 68.89% 1024 1024, 1024 PART Five

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