AI’s Next Job? Recruiting People To Train More AIhttps://t.co/iCuBRSM2bF #Cloud100 https://t.co/W53oox589X ...

Market Dynamics & Industry Trends - Demand is outpacing supply in the computing infrastructure space, particularly for parallelized computing, driven by the traction in artificial intelligence for both training new models and inference [1][3][4] - The industry is experiencing a systemic imbalance, leading to a planetary-scale buildout of computing infrastructure expected to support AI for the next 50 years [8][9][10] - Inference demand is continually increasing and now accounts for over 50% of compute usage, while training demand remains strong [12][13][28] - The newest GPU architectures are used for bleeding-edge training, with prior generations being utilized for inference, showing a generational shift in usage [15] Company Strategy & Business Model - The company focuses on delivering comprehensive supercomputer solutions, emphasizing that delivering 97% is insufficient [4][5] - The company is diversifying its client base beyond hyperscalers like Microsoft and penetrating additional layers of the enterprise space, including VFX and life sciences [16][17][18] - The company structures its business around long-term contracts to insulate from short-term spot price variance in computing [25] - The company has seen a 900 basis points (9%) decrease in the cost of capital in its latest delayed draw facility for non-investment grade clients [26] Operational Challenges & Infrastructure - The key bottleneck is the powered shell, encompassing building, cooling, and electricity distribution [5] - The company anticipates facing a cycle of shortages, moving from powered shell bottlenecks to silicon or networking shortages [6] - Hyperscale clients are extending and broadening their contractual relationships, indicating a broader effort to address the systemic imbalance [22][23]

Training Improvement - The model should feed the results back into training to improve the one-shot probability [1]

LLM Training Stages - LLM 从零开始训练包含四个阶段 [1] - 第一步是使用随机初始化的模型 [2] - 之后在大规模语料库上进行预训练 [2] - 使用指令微调使其能够遵循命令 [2] - 使用偏好和推理微调来优化响应 [2]

LLM Training Stages - The document outlines 4 stages of training LLMs from scratch [1] Visual Aids - The explanation includes visuals for clarity [1]

AI Workload Challenges & Requirements - AI workloads differ from traditional cloud workloads due to the need for high throughput and low latency, especially in large language model training involving thousands of GPUs communicating with each other [2][3][4] - Network glitches like packet drops, congestion, or latency can slow down the entire training process, increasing training time and costs [3][5] - Networks must support small to large-sized clusters for both inference and training workloads, requiring high performance and reliability [8] - Networks should scale up within racks and scale out across data halls and data centers, while being autonomous and resilient with auto-recovery capabilities [9][10] - Networks need to support increasing East-West traffic, accommodating data transfer from various sources like on-premises data centers and other cloud locations, expected to scale 30% to 40% [10] OCI's Solution: Backend and Frontend Networks - OCI addresses AI workload requirements by implementing a two-part network architecture: a backend network for high-performance AI and a frontend network for data ingestion [11][12] - The backend network, designed for RDMA-intensive workloads, supports AI, HPC, Oracle databases, and recommendation engines [13] - The frontend network provides high-throughput and reliable connectivity within OCI and to external networks, facilitating data transfer from various locations [14] OCI's RDMA Network Performance & Technologies - OCI utilizes RDMA technology powered by RoCEv2, enabling high-performance, low-latency RDMA traffic on standard Ethernet hardware [18] - OCI's network supports multi-class RDMA workloads using Q-cure techniques in switches, accommodating different requirements for training, HPC, and databases on the same physical network [20] - Independent studies show OCI's RDMA network achieves near line-rate throughput (100 gig) with roundtrip delays under 10 microseconds for HPC workloads [23] - OCI testing demonstrates close to 96% of the line rate (400 gig throughput) with Mi300 clusters, showcasing efficient network utilization [25] Future Roadmap: Zeta-Scale Clusters with AMD - OCI is partnering with AMD to build a zeta-scale Mi300X cluster, powering over 131,000 GPUs, which is nearly triple the compute power and 50% higher memory bandwidth [26] - The Mi300X cluster will feature 288 gig HBM3 memory, enabling customers to train larger models and improve inferencing [26] - The new system will utilize AMD AI NICs, enabling innovative standards-based RoCE networking at peak performance [27]

The Paradox of Choice in Fitness and Performance - The fitness industry presents an overwhelming number of choices, including gyms, studios, apps (117,000), and training formats, leading to over-choice and decision paralysis [4] - Elite athletes also face the paradox of choice when selecting training gears, recovery protocols, and nutrition protocols, making it difficult to determine which information to trust [5][6] - Too many choices can lead to confusion, impacting attention, clarity, and mental energy, resulting in cognitive overload [8] - Over-reliance on data without reducing doubt can be detrimental to performance [10] The Autonomic Nervous System and Peak Performance - Peak performance is not just about working harder but about toggling between stress (sympathetic nervous system) and recovery (parasympathetic nervous system) [16][17] - Activating the parasympathetic nervous system through breath work or mindfulness can positively impact heart rate variability and cortisol levels [15] - Implementing simple recovery protocols, such as limiting screen time and social media, can aid in mental recovery [12] Simplicity and Intention - Returning to simplicity and rhythm can be beneficial in a world that constantly encourages doing more [18] - Pausing and moving forward with intention is crucial in a world of constant options [19]

Personal Development & Performance - Embracing pain and enjoying the process is key to unlocking potential and achieving breakthroughs [7][9][16][21] - Acknowledging the issue or problem is the first step towards improvement [10] - Training with individuals who are better can accelerate personal growth [11][15] - The formula of acknowledging issues, embracing the process, and training with the best can be applied to various challenges [11][12] Team Dynamics & Leadership - Individual leadership involves making a choice to let pain build rather than break, emphasizing resilience [20] - Overcoming challenges and embracing pain can inspire others and foster a sense of community [17][18] Mindset & Problem Solving - Reframing problems as opportunities for character building can shift perspective and improve resilience [19] - Pain is not permanent, and failure is not final; strength is measured by the ability to rise after falling [20]