AI Compute Demand & GPU Market - AI 计算需求增长速度是效率增长速度的两倍 [1] - 除非 GPU 效率提高，否则 GPU 将成为人工智能领域最受欢迎的商品 [1] Crypto Protocols Benefiting from GPU Demand - Livepeer 是一个去中心化的视频流网络，利用 GPU 资源进行高效的实时视频转码和处理，收入与价格合理相关 [1] - USDai_Official 是 GPU 支持的借贷协议 [2] - Gaib_ai 将企业 GPU 收益代币化为可交易资产 [2] - AethirCloud 提供企业级 DePIN GPU 云 [2] - Render Network 提供用于 3D 渲染和视觉效果的 GPU 计算 [2] - io.net 提供的计算比 AWS 便宜 70% [2] - 0G_labs 是一个具有 GPU 集群的模块化 L1，用于可验证的计算 [2] - SpheronFDN 通过无需许可的计算市场产生需求 [2] - Akash Network 是一个用于云计算资源的开源市场 [2] - Theta Network 的 EdgeCloud 利用用户 GPU 进行去中心化的视频渲染和 AI 推理 [2] - Golem Project 是一个点对点计算市场，在全球范围内出租闲置 GPU 用于渲染/AI 任务 [2] TAO + Subnets - SN 64: Chutes_ai [2] - SN 51: Lium_io [2] - SN 27: Neural_internet [2] - SN 12: ComputeHorde [2]

AI Compute Demand & GPU Market - AI compute demand is growing at twice the rate of efficiency growth, potentially leading to GPU shortages [1] - To meet current AI compute demand, an estimated $500 billion must be invested in data centers annually until 2030 [2] Crypto Protocols Benefiting from GPU Demand - Livepeer, a decentralized video streaming network, benefits from GPU resources for video transcoding and processing [1] - Several crypto protocols are positioned to capitalize on GPU demand, including GPU-backed lending protocols like USDai_Official, enterprise GPU yield tokenization platforms like gaib_ai, DePIN GPU clouds like AethirCloud, and GPU compute providers for 3D rendering like Rendernetwork [2] - Other protocols include ionet (compute provider), 0G_labs (modular L1 with GPU clusters), SpheronFDN (compute marketplace), akashnet_ (cloud computing marketplace), and Theta_Network (decentralized video rendering and AI inference) [2] TAO + Subnets - TAO subnets like chutes_ai (SN 64), lium_io (SN51), neural_internet (SN27), and ComputeHorde (SN12) are relevant in the context of GPU compute [2]

GPU Profiling & Performance Optimization - The industry emphasizes improving performance and saving costs by optimizing software, potentially reducing server usage by 10% [4] - Sampled profiling is used to balance data volume and continuous monitoring, with examples of sampling 100 times per second resulting in less than 1% CPU overhead and 4MB memory overhead [5] - The industry highlights the importance of production environment profiling to observe real-world application performance with low overhead [8] - The company's solution leverages Linux EVPF, enabling profiling without application instrumentation [9] Technology & Metrics - The company's GPU profiling solution uses Nvidia NVML to extract metrics, including overall node utilization (blue line), individual process utilization (orange line), memory utilization, and clock speed [11][12] - Key metrics include power utilization (with power limit as a dashed line), temperature (important to avoid throttling at 80 degrees Celsius), and PCIe throughput (negative for receiving, positive for sending, e g 10 MB/s) [13][14] - The solution correlates GPU metrics with CPU profiles collected using EVPF to analyze CPU activity during periods of less than full GPU utilization [14] GPU Time Profiling - The company introduces GPU time profiling to measure time spent on individual CUDA functions, determining start and end times of kernels via the Linux kernel [18] - The solution displays CPU stacks with leaf nodes representing functions taking time on the GPU, with colors indicating different binaries (e g blue for Python) [19][20] Deployment & Integration - The company's solution can be deployed using a binary on Linux, Docker, or as a DaemonSet on Kubernetes, requiring a manifest YAML and token [21] - Turbo Puffer is interested in integrating the company's GPU profiling to improve the performance of their vector engine [22]