LLM Infrastructure
Model selection, hosting, fine-tuning, cost optimization, and scaling LLM-powered systems in production.
Running large language models in production requires careful infrastructure planning—from model selection and hosting to fine-tuning, cost optimization, and GPU provisioning. Explore practical guides on building reliable, scalable LLM infrastructure that balances performance, cost, and latency for real-world applications.
464 articles in this category
AI is the New Key to Unlocking the American Dream
This panel brought together Taylor Stockton (DOL Chief Innovation Officer), Josh Allen (Walmart Academy), and Naria Santa Lucia (Microsoft Elevate) to discuss AI's impact on the labor economy and workforce.
Coffee with Crow: The AI Roadmap Ahead: Pro Human Learning & Work
ASU President Michael Crow moderates a conversation with will.i.
State Chiefs on Leadership — Aimee Guidera (ASU+GSV)
Former Virginia education secretary Aimee Guidera joins a candid ASU+GSV 2026 panel of state education chiefs on system leadership, reform, and hard-won lessons.
State Chiefs on Leadership — Angélica Infante-Green
Rhode Island commissioner Angélica Infante-Green joins a candid ASU+GSV 2026 panel of state education chiefs on system leadership, reform, and lessons learned.
Turning Goals Into Scalable Systems: Statewide Career Navigation in Action
This panel explored the practical challenges of building statewide career navigation systems that actually reach students.
From Content to Conversation
Victor Riparbelli, CEO and co-founder of Synthesia, presented the evolution of AI video from simple avatar-based content creation to interactive "Video Agents" that transform learning from passive consumption to two-way conversation.
Clear Eyes, Full Hearts, Can't Lose... Texas Education Policy
This panel showcased Texas as a national model for place-based education partnerships, featuring F. Mike Miles (Houston ISD superintendent), Todd Williams (Commit Partnership), Jeff Edmonson (Ballmer Group), and Anne Wicks (Bush Institute) as moderator.
Multiple Choice... What's Love Got to Do With It?
A panel moderated by Michelle Rhee explores the state of school choice in America, featuring perspectives from charter school advocates, operators, and a Chicago-based leader confronting anti-choice political forces.
Accelerator Announcement with Jonathan Hage
Jonathan Hage announced the launch of "Launched," described as the world's first true education innovation marketplace.
Beg, Borrow or Steal…A New American Talent System for an AI Disrupted World
Moderated by Jon Schnur (America Achieves), this panel examined how the U.S.
Beyond the Novelty: Evaluating AI-Powered Career Navigation Tools
A five-person panel moderated by Rowan Trollope (BrightBound) explored how AI-powered career navigation tools can reduce inequalities rather than reinforce them.
Disagreeing Better
Harvard Kennedy School professor Julia Minson presented the core ideas from her new book "How to Disagree Better," arguing that persuasion fundamentally does not work and that the real goal of constructive disagreement should be getting the other per
FUSION with Michael Moe
GSV founder Michael Moe delivers the opening keynote of the 17th annual ASU-GSV Summit, framing education's transformation through the lens of "fusion" -- the convergence of man and machine, learning and earning, physical and digital.
From Wedge to Leading Edge... Rahm Emanuel on the Education Reset
Former Ambassador Rahm Emanuel discusses his vision for education reform in America, drawing on his experience as Mayor of Chicago, White House Chief of Staff, and potential 2028 presidential candidate.
Why Universities Are Building MCP Data Layers Before Deploying AI Agents
The universities scaling AI fastest share one trait: they built their MCP data layer first. Here's why the integration architecture matters more than the AI model you choose.
From Pilot to Platform: How Universities Are Deploying AI Agents Across Every Department
The AI pilot era is over. Universities that are winning the AI transition have moved from isolated chatbot experiments to institution-wide agentic infrastructure — with full data control and measurable outcomes.
How Universities Are Building Institutional AI Memory with MCP in 2026
How forward-thinking universities are using the Model Context Protocol to connect their SIS, LMS, and CRM data into a unified AI memory layer — and why it matters for institutional competitive advantage in 2026.
Why Agentic AI Programs Stall at Pilot — and the Architecture That Scales
67% of enterprises say security risk is their #1 blocker to scaling AI. This post diagnoses why agentic AI pilots succeed but scale fails — and what the architectural answer looks like.
Meta Muse Spark and the Parallel Reasoning Architecture Shift
Meta's Muse Spark introduces parallel agent reasoning to frontier AI. Here's what the architecture means and why it changes how organizations should evaluate models.
Open-Source AI Just Beat Closed-Source on the Hardest Coding Benchmark
GLM-5.1 from Zai just scored 58.4 on SWE-Bench Pro — beating Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro. Here's what the open-source surge means for organizations deploying AI agents.
The AI Training Data Supply Chain Is More Fragile Than You Think
The Mercor data breach exposes a hidden vulnerability in how the world's most powerful AI models are built. Here's what organizations need to understand about the AI training data supply chain.
How Microsoft Purview Extends Data Governance to OpenClaw AI Agents
Microsoft Purview's data security capabilities now extend to enterprise AI apps — including OpenClaw instances registered through Microsoft Entra. Here's how the integration works and why it matters for organizations deploying AI agents at scale.
Google Gemma 4 Switches to Apache 2.0: What This Means for Organizations Running Their Own AI
Google's Gemma 4 release under Apache 2.0 marks a turning point for organizations that want to run frontier-class AI on their own infrastructure. Here's what changed, why it matters, and how to evaluate open-weight models for production use.
What Anthropic's Claude Lockdown Teaches Us About Owning Your AI Infrastructure
Anthropic just restricted Claude subscriptions from third-party tools. Google's Gemma 4 went truly open-source. An AI agent found a 23-year-old Linux vulnerability. Three stories from one week that explain why organizations need to own their AI infrastructure.