Tech • AI • Robotics
33 articles analyzed by AI / 44 total
Detailed methods such as NVIDIA DCGM and enhanced profiling techniques are presented to measure actual GPU usage during AI model training. This allows engineering teams to identify GPU saturation points accurately and optimize training pipelines at scales up to 500 GPUs, surpassing standard tools like 'nvidia-smi' which often provide incomplete data.
This article analyzes the growing complexity in AI infrastructure due to workload routing challenges across heterogeneous compute clusters. It explains architectural tradeoffs and introduces routing algorithms used to optimize GPU/TPU resource utilization and latency, providing actionable insights for engineering teams designing scalable AI serving infrastructure.
Based on 4,265 sessions using local LLM agents such as Claude Code and Codex, this article reveals that 75% of local runs fail on consumer hardware due to 16GB RAM constraints. The month-long measurement project identifies memory as the critical bottleneck limiting robustness and scalability of local agent deployments, providing important operational insights for engineering teams aiming to build reliable local AI tooling.
This article examines the emerging concept of cooling-defined infrastructure in AI data centers, where cooling system design directly influences compute density and operational efficiency. It discusses trade-offs in liquid cooling technologies and their impact on sustained GPU performance and overall data center cost optimization.
This article explores how coding agents like Claude Code and Codex can be repurposed for non-programming tasks through prompt engineering and chain construction. It covers practical agent application design patterns, highlighting the potential to extend coding agent workflows beyond development and into broader AI-driven business tooling.
The piece argues that AI infrastructure scalability and reliability heavily depend on high-performance network design. It details networking technologies like InfiniBand and NVLink that support low-latency communication in multi-GPU setups, offering important infrastructure architecture recommendations for teams building robust AI data centers.
At Black Hat USA 2026, agent exploitation was identified as a distinct new AI infrastructure discipline focused on securing and managing AI agents against attacks. The article discusses emerging security challenges, guardrail implementations, and infrastructure practices necessary to mitigate agent-level vulnerabilities in production AI systems.
Meta, led by Mark Zuckerberg, plans to invest up to $145 billion in AI infrastructure by 2026, focusing on expanding data center capacity, GPU farms, and AI-specific tooling. This significant financial commitment highlights Meta’s strategy to enhance its competitive AI capabilities and scale production-grade AI deployments.
The opinion piece advocates for New Jersey to establish standards for responsible AI infrastructure development, emphasizing governance, compliance, and security frameworks. It highlights the engineering leadership role in designing AI systems that balance innovation with ethical and regulatory constraints in production environments.