Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising • 60% 10% 21% 15% 0% Details on Page 293 USA – LLM #1 China USA – LLM #2 AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising Than Ever? Yes, It Is • AI User + Usage + CapEx Growth = Unprecedented • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising •

by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning training costs, and efficient inference throughput (Figure 1(b)), simultaneously. We construct a high-quality and multi-source pre-training corpus consisting of 8.1T tokens. Compared with the corpus used supporting efficient inference. For FFNs, we adopt the DeepSeekMoE architecture (Dai et al., 2024), a high-performance MoE architecture that enables training strong models at an economical cost. An illustration

large-scale energy 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 lithium-ion batteries (LIBs)have become mainstream energy storage devices with their high output voltage, high energy density, and long cycle life. In order to meet the strong demand for further improving that provide lithium-ion batteries with safe and stable cyclic performance, while providing high capacity and high voltage curves, has sparked in-depth research and discussion. As a promising candidate for

up the LLM to predict the right sequence of tokens. Prompt engineering is the process of designing high-quality prompts that guide LLMs to produce accurate outputs. This process involves tinkering to find temperature (T), emphasizing a single, preferred temperature with high certainty. A higher Gemini temperature setting is like a high softmax temperature, making a wider range of temperatures around the irrelevant–the most probable token becomes the next token predicted. If you set temperature extremely high (above 1–generally into the 10s), temperature becomes irrelevant and whatever tokens make it through

and automate workflows, agents are able to perform the same workflows on the users’ behalf with a high degree of independence. Agents are systems that independently accomplish tasks on your behalf. A workflow multiple agents 
 (see Orchestration). 10 A practical guide to building agents Configuring instructions High-quality instructions are essential for any LLM-powered app, but especially critical for agents. Clear safeguards Assess the risk of each tool available to your agent by assigning a rating—low, medium, or high—based on factors like read-only vs. write access, reversibility, required account permissions, and

Set bold 
 automation goals Most processes involve a lot of rote work, ripe for automation. Aim high. Let’s drill down into each of these, with customer stories as examples. 5 AI in the EnterpriseLesson citations, every time. Faster outcomes Less manual editing or re-checking means your teams can focus on high-value tasks. 15 AI in the EnterpriseLesson 5 Get AI in the hands of experts BBVA takes an expert-led consistent platform that uses natural language as a central interface. Developers now build consistently 
 high-quality apps, faster, without having to get into the source code. Security, guardrails, and routing

optimized Open source, automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge Edge FPGA Cloud FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to Engineering intensiveMachine Learning based Program Optimizer TVM: Learning-based Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads

DNN Specific Instruction 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

contributors at FB and elsewhere- Performance matters a lot - Heterogenous computing environment - High variety of workloads - Ever-increasing set of primitives (over 500 aten kernels) - Interpreter

Models) Host Device High-Level 人 ORGREEE Te Conv2D mized RE -一 一 QQ octoML Transformer Improvements