(KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger 7 2.1.3 Decoupled Rotary Position Embedding . . . . . . . . . . . . . . . . . . . . 8 2.1.4 Comparison of Key-Value Cache . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 DeepSeekMoE: Training D.1 Ablation of MHA, GQA, and MQA . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 D.2 Comparison Between MLA and MHA . . . . . . . . . . . . . . . . . . . . . . . . . 31 E Discussion About Pre-Training

Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising • AI Usage + Cost + Loss Growth = Unprecedented • AI Monetization Threats = Rising Competition + Open-Source Momentum Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising 3 Cost of Key Technologies Relative to Launch Year % of Original Price By Year (Indexed to Year 0) Note: International Federation of Robotics Industrial Robots Installed Details on Page 289 AI Usage + Cost + Loss Growth = Unprecedented 4 Leading USA-Based AI LLM Revenue vs. Compute Expense Note: Figures

77 | | | Depthwise Convolution Workload Performance Alios TVM @ ARM CPU INT8 Performance Comparison @ rasp 3b+ AARCH64 aoo0 8.87 sm ao 7m am sm 3.83 om ao 2.08 2 with LLVM to simulate GEMM microkernel /NiiOS ! 驱动万物智能 Alios TVM @ ARM CPU FP32 Performance Comparison AARCH64 12 135 117 工1 1 1.07 国

return new_call© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Comparison of Two Options Op-level annotation ● Simple and easy to implement 👍 ● One op per subgraph results

Accuracy© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Performance Comparison • Metric – Latency in ms for batch size = 1 • 1.7x speedup on Inception asymmetric quantized

Different models have different strengths and tradeoffs related to task complexity, latency, and cost. As we’ll see in the next section on Orchestration, you might want to consider using a variety 
 of performance baseline 02 Focus on meeting your accuracy target with the best models available 03 Optimize for cost and latency by replacing larger models with smaller ones 
 where possible You can find a comprehensive

Operators Algorithm &Schedule CUDA TOPI Backends Machine Learning Automated Optimizer Schedule explorer Cost model Mali TOPI ROCM TOPI PVRTOPI Alibaba Al.Labs 阿里巴巴人工智能实验室 PVR TOPI > TOPI for PVR,including

because we set bold automation goals from the start, instead of accepting inefficient processes as a cost of doing business. 21 AI in the EnterpriseConclusion Learning from each other As the previous examples

includes the chain of thought reasoning, which means more output tokens, which means predictions cost more money and take longer. To explain the following example in Table 11, let’s first try to create