21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e (Tokens/Sec) (b) Figure 1 | (a) MMLU accuracy vs. activated parameters, among different open-source models. (b) Training costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2 adoption and utilization of LLMs. In order to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient

• AI Usage + Cost + Loss Growth = Unprecedented • AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet on Page 975 …Charts Paint Thousands of Words… AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 5 Leading USA LLMs vs. China LLM Desktop User Share Note: Data Electric Power Computer Memory AI Inference AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 5.1 China vs. USA vs. Rest of World Industrial Robots Installed

and reviewing code 48 What about multimodal prompting? 54 Best Practices 54 Provide examples 54 Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control specific model, regardless of whether you use Gemini language models in Vertex AI, GPT, Claude, or an open source model like Gemma or LLaMA. Besides the prompt, you will also need to tinker with the various of description of what this article should contain. Output 1. **The Evolution of Arcade Cabinet Design:** This article would explore the evolution of arcade cabinet designs, from the early wood and

Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated end-to- end optimization framework for deep learning.TVM Stack High-Level tvm.sum(A[k, y] * B[k], axis=k)) HW Interface Specification by Tensor Expression TensorizationVTA: Open & Flexible Deep Learning Accelerator • Runtime JIT compile accelerator micro code • Support heterogenous hardware design full stack open source Current TVM Stack VTA Runtime & JIT CompilerTSIM: Support for Future Hardware Current TVM Stack New NPU Runtime TSIM Driver TSIM Binary New Hardware Design in Verilog

2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Outline • QNN Dialect • Design • Operators • Results on Intel Cascade Lake© 2019, Amazon Web Services, Inc. or its Affiliates extent)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. QNN Dialect • Design operators that satisfy many framework operators • qnn.quantize, qnn.dequantize, qnn.requantize hosted models • MXNet Pre-quantized Models • Tested internally with MxNet + MKLDNN path • Will open RFC in a month© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Evaluation

Q OctoML Open Source at O〇ctoML TVM Meetup 11/8/2019 Jared Roesch OctoML is a new company building DL deployment solutions using the Apache (incubating) TVM project. A goal is to nurture the TVM community combined experience in computer systems design and machine learning tr tvm 。 @zxnet 和os 全 W Open Source at OctoML ee We are big believers in the power of open source o 5S$ponsoring multiple employees

guide to 
 building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction Large Otherwise, a deterministic solution may suffice. 6 A practical guide to building agents Agent design foundations In its most fundamental form, an agent consists of three core components: 01 Model The

Pursued By A Bear - 3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter, TVM and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified

AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Considering You... Design and manufacture a deep learning chip which achieves amazing performance on widely-used operators

垂直领域优化：针对特定领域 （如医疗、法律）进行优化， 提供高精度结果。  长文本处理：擅长处理长文本 和复杂文档，适合专业场景。  定制化能力：支持用户自定义 训练和微调，适应特定需求。 Open AI o3 mini  小型化设计：轻量级模型， 适合资源有限的环境。  快速响应：优化推理速度， 适合实时交互场景。  通用性强：适用于多种自 然语言处理任务，如对话 生成和文本理解。 年春运（2025年1月14日到2月8日） 相关数据（如日期、全社会跨区域人员流动量、铁路客运 量、公路人员流动量、水路客运量、民航客运量等）”完 成数据提取并写入文件“2025春运数据.txt” Open AI o3mini 响应速度快，能够高效提 取所有需求链接，输出完 整可运行python脚本，代 码运行后生成文件，但数 据采集结果为空。 DeepSeek R1 能够提取所有网址并进行 。 爬虫数据采集  目前DeepSeek R1、Open AI o3mini、Kimi k1.5支持联网查询网址，Claude 3.5 sonnet暂不支持；  四个模型均能根据上传的网页代码，对多个网址链接进行筛选、去重，完全提取出符合指令要求的所有网址链接并形成列表；  在复杂爬虫任务上，DeepSeek R1与Open AI o3min生成的代码均能正常执行数据采集任务，o3响应速度更快，R1数据采集结果更加完