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-Ready Architecture for Healthcare: Why Hospitals Need AI Platforms They Control
Healthcare systems are deploying AI tools that send PHI to third-party servers. That's not AI-ready architecture — it's a HIPAA exposure the CISO hasn't quantified yet.
AI-Ready Architecture for Higher Education: Why Universities Need Modular Platforms They Own
Universities are buying AI platforms they can't inspect, can't customize, and can't leave. That's not AI-ready architecture — it's a new kind of vendor lock-in.
AI-Ready Architecture for Law Firms: Why Legal AI Must Be Air-Gapped and Owned
Law firms are deploying AI tools that send privileged client data to third-party servers. That's not AI-ready architecture — it's a potential privilege waiver.
Why 'AI-Ready' Architecture Means Owning Your Platform, Not Renting It
Every vendor calls their platform 'AI-ready' and 'modular.' Most of them mean the same thing: an API, a plugin marketplace, and a monthly invoice. That's not modularity — it's a dependency with a storefront.
Sovereign AI for Federal Agencies: Why Early Access to Vendor Models Isn't a Security Strategy
Federal agencies are accepting 'early access' to commercial AI models as a security posture. It isn't. Here's what sovereign AI actually looks like.
Why Federal Agencies Are Rethinking Per-Seat AI: The Case for Sovereign Infrastructure
Federal agencies face a stark choice: pay $30+/user/month for cloud AI they don't control, or build sovereign AI infrastructure inside their own perimeter.
One Agent Per Student: The Infrastructure Behind Truly Personalized Learning
The shift from shared AI chatbots to dedicated per-student AI agents is redefining what personalized learning actually means — and the infrastructure required to deliver it.
Why 40% of Agentic AI Projects Will Be Cancelled by 2027 — and How to Be in the Other Half
Gartner's first Hype Cycle for Agentic AI shows 40% enterprise adoption and 40% cancellation rates — on the same chart. Here is what separates the organizations that will still have working systems in 2027.
Why Federal Agencies Need Sovereign AI Infrastructure in 2026
Google's classified deal with the Pentagon signals a new era for government AI. Here's what federal agencies need to get right.
Why Enterprise AI Consolidation Is Accelerating — And What the Winners Are Doing Differently
Enterprise AI budgets are rising but vendor lists are shrinking. The organizations pulling ahead are consolidating around infrastructure they own, not rent.
Why 95% of Enterprise AI Pilots Fail — and What the 5% Do Differently
MIT's 2026 study found 95% of enterprise GenAI pilots fail to deliver ROI. The organizations that succeed share one pattern: agents connected to real institutional data, not chatbots with system prompts.
The Agentic Government: Why 250,000 AI Agents Are Just the Beginning
A sovereign nation has committed to running 50% of government operations on agentic AI within two years — with 250,000 agents already active. Here's what that shift means for public institutions globally, and why the gap between 'AI strategy' and 'AI infrastructure' is where governments will either lead or fall behind.
The Enterprise AI Agent Inflection Point: What NVIDIA, Google, and OpenAI Just Shipped
In one week, NVIDIA, Google, and OpenAI each launched enterprise agent platforms. Here's what happened, why it matters, and what organizations should look for before deploying.
The AI Governance Mirage: Why Enterprises Are Building Control Planes From Scratch
72% of enterprises believe they have adequate AI governance. VentureBeat's Q1 2026 research says most don't. Here's what the organizations getting it right are doing differently.
How Enterprise Teams Are Replacing AI Chatbots with Autonomous Agent Architectures in 2026
The Stanford AI Index 2026 confirmed what enterprise leaders are learning the hard way: autonomous agents now outperform expectations, but most organizations are still buying chatbots. Here's what the shift to agentic architecture actually looks like in practice.
From Chatbots to Agents: How Enterprise Organizations Are Deploying Autonomous AI in 2026
Gartner projects 40% of enterprise apps will embed autonomous AI agents by end of 2026 — up from less than 5% in 2025. Here is what that transition actually looks like in production, and what organizations need to build it right.
Sovereign AI Agents for Government: Why Federal Agencies Are Choosing Infrastructure They Own
Federal agencies building sovereign AI infrastructure — owning their code, choosing their LLMs, deploying on their own networks — are creating strategic compounding advantages that per-seat SaaS subscriptions cannot match.
The Governance Gap: Why Enterprise AI Agents Succeed or Fail in Production
Most enterprise AI pilots fail in production for operational reasons, not technical ones. This is what governance-first agent deployment actually looks like in 2026.
Why Enterprise AI Is Moving from Per-Seat Licensing to Agentic Operating Systems
Per-seat AI licensing is breaking at enterprise scale. Organizations are moving to agentic AI operating systems — platforms they own, deploy anywhere, and scale without per-seat cost penalties.
Coffee with Crow: Building A Future Where Everyone Can Work with AI
A panel featuring former U.S.
A Student-First, AI-Native Vision for the Future
A senior leader from Western Governors University (WGU) presented a comprehensive vision for how AI can fundamentally transform higher education from a provider-centered model to a learner-centered one.
But What are You Doing for YOUR Kids?
This panel, moderated by Patrick Methvin (Gates Foundation), brought together education leaders who are also parents -- Dacia Toll (CourseMojo), Michael Sorrell (Paul Quinn College), and Stephen Jull (Teach for All) -- to explore the disconnect betwe
Why Enterprise AI Integration Keeps Failing — And How MCP Fixes the Architecture
Most enterprise AI deployments fail at the integration layer, not the AI layer. The Model Context Protocol (MCP) is changing the architecture — and why it matters for every organization deploying AI at scale.
Career-Connected Learning
This panel on career-connected learning featured CEOs from four education companies -- James Rhyu (Stride), Jamie Candee (Edmentum), Krishna Kumar (Simplilearn), and Steve Daly (Instructure) -- moderated by Tony Won (Reach Capital).