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

JP Morgan End-to-End AI Modernization – 2023-2025E, per JP Morgan We have high hopes for the efficiency gains we might get [from AI]… …Certain key subsets of the users tell us they are gaining several alerts. It leverages machine learning to improve decision-making at the restaurant level, enhancing efficiency, reducing waste, and supporting staff productivity. ‘Traditional’ Enterprise AI Adoption = Rising students across a mix of STEM and non-STEM disciplines; only answers from 18-24 year olds used. Sample includes both AI users and non-users but excludes “AI rejectors” – defined as non-users with little

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

scale up to significant business impact. But scaling up also meant using more tokens. To increase efficiency, OpenAI and Indeed 
 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

and top-P criteria are candidates for the next predicted token, and then temperature is applied to sample from the tokens that passed the top-K and top-P criteria. If only top-K or top-P is available, the m) and the google-search-results pip packages. Prompt Engineering February 2025 38 To run this sample you must create a (free) SerpAPI key from https://serpapi.com/manage- api-key and set an environment

models, like o1 or o3-mini, to automatically generate instructions from existing documents. Here’s a sample prompt illustrating this approach: Unset 1 “You are an expert in writing instructions for an LLM