= Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising • AI Usage + Cost + Loss Growth = Unprecedented • AI Monetization China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising 3 Cost of Key Technologies Relative to Launch Year % of competitive. Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically

through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum even with only 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 . p S e e k - V 2 . 0 20 40 60 80 100 Activated Parameters (Billions) 55 60 65 70 75 80 Performance (MMLU) DeepSeek-V2 DeepSeek 67B LLaMA 1 33B LLaMA 1 65B LLaMA 2 13B LLaMA 2 34B LLaMA 2

Engineering February 2025 25 Step-back prompting Step-back8 prompting is a technique for improving the performance by prompting the LLM to first consider a general question related to the specific task at hand thought appears to improve robustness when moving between different LLM versions. Which means the performance of your prompt should drift less between different LLMs than if your prompt does not use reasoning (APE). This method15 not only alleviates the need for human input but also enhances the model’s performance in various tasks. You will prompt a model to generate more prompts. Evaluate them, possibly alter

1.31 -35 1 129 中131 124有23152136 2 1.14 am omo oo Convolution Workload Performance AiOS 1驱动万物智能 Alios TVM @ ARM CPU INT8 Depthwise Convolution 。， NHWC layout 。 Using TVM schedule 09工08 工区 0.77 0.77 | | | 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 to cooperate 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

well is to build your agent prototype with the most capable model for every task to establish a performance baseline. From there, try swapping in smaller models to see 
 if they still achieve acceptable fail. In summary, the principles for choosing a model are simple: 01 Set up evals to establish a performance baseline 02 Focus on meeting your accuracy target with the best models available 03 Optimize for complex workflows, splitting up prompts and tools across multiple agents allows for improved performance and scalability. When your agents fail to follow complicated instructions 
 or consistently select

计算平台事业 。TensorCore 。A revolutionary technology that delivers groundbreaking AI performance. 。 Performs /mxeo-Drecsion matrix multiply and accumulate in a single operation. Background CT wmma:mma_syncfcompute Jocalloj B_shareal_locollol A_sharea_locolloj compute_locallo族 了 了 Performance Optimization 计划了全事业部 “Same as non-TensorCore CUDA codegen 。Auto tune tiling sizes storage align to reduce bank conflicts of shared memory 。 Virtual threads for data reuse (on going) Performance on V100 (FP16) 计算平台事业部 COMPUTING PLATFORM 512, 16, 512 512, 32, 512 512, 64, 512 512, 128, 512

storage technologies allow intermittent renewable energy to replace traditional energy. High-performance secondary batteries are one of the most promising candidates for large-scale energy storage intermittent further improving its electrochemical performance, the search for sustainable anode materials that provide lithium-ion batteries with safe and stable cyclic performance, while providing high capacity and renewable energy sources to replace traditional forms of energy. Among these technologies, high-performance secondary batteries are one of the most promising solutions. Lithium-ionbatteries (LIBs), in particular

We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter 
 time frames. 02 Automating routine product improvements. That means shipping updates regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and job matching engine against the GPT-powered version with the new, customized context. 
 The performance uplift was significant: A 20% increase in job applications started A 13% uplift in downstream

c_char_p(graph_path.value).value layout = c_char_p(output_layout.value).value … >> 12© Copyright 2018 Xilinx Performance Pipelines ˃ References to our latest results: https://github.com/Xilinx/AI-Model-Zoo (embedded measurements we track: Latency & Throughput ˃ ML pipeline contains multiple stages, performance limited by slowest one ˃ Performance results based on Xilinx own runtime pipeline available in github (https://github

TVM at Facebook Lots of contributors at FB and elsewhere- Performance matters a lot - Heterogenous computing environment - High variety of workloads - Ever-increasing set of primitives (over 500 500 aten kernels) - Interpreter methods not delivering generalized performance 2 Why TVM? XTVM for Speech Synthesis - WaveRNN-style model architecture - Autoregressive sampling net running at faster