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.
587 articles in this category
Implementation Requirements for AI Agents on Your IT Stack
What are the implementation requirements for deploying custom AI agents within an organization's existing IT infrastructure? The six requirement areas — identity, data integration, compute, guardrails, audit, and operations — with the concrete checklist for each.
Enterprise AI OS Pricing vs Standard Cloud AI Services
How does enterprise AI operating system pricing compare to standard cloud AI services? The three pricing shapes, the same workload priced each way, and why the OS layer should cost like the API — not like a per-seat suite.
AI Platforms for Universities That Keep Data On-Premise
What are the best AI platforms for universities that need to keep student data on-premise? The direct answer, the FERPA case for on-premise, the honest vendor landscape, and the cost math at a 30,000-student university.
AI OS Platforms That Deploy Agents on Your Infrastructure
Which AI operating system platforms let you deploy AI agents on your own infrastructure? A direct answer, the honest vendor landscape, what 'your own infrastructure' actually means, and the requirements checklist buyers should use.
MiniMax's 2.7-Trillion-Parameter Model Proves Enterprise AI Must Be Model-Agnostic
MiniMax is preparing a 2.7-trillion-parameter open-source model — the largest ever. Here is why enterprises that locked into a single model vendor are about to pay for it.
Why K-12 Districts Need AI Infrastructure They Own — Not Another Vendor Subscription
Both the US and China are now restricting access to frontier AI models. K-12 districts relying on vendor-hosted AI subscriptions face the same risk — and there is a better path.
Paying for Tokens Isn't Buying AI Value — Own the Stack
Token spend is a cost, not an outcome. The organizations getting real AI value run an LLM-agnostic architecture and an owned application layer, so every dollar of usage compounds into an asset they keep.
AI Ownership: The Four Questions Every Buyer Must Ask
The value of enterprise AI concentrates in the application layer — the ontology — not the model. Four ownership questions (data, weights, application layer, compute) decide whether that value is yours or your vendor's.
Why Government Agencies Cannot Afford to Rent Their AI Infrastructure
AWS and Microsoft just committed $3.5B to forward-deployed AI engineering. Government agencies that rent this infrastructure instead of owning it are building dependency into their most sensitive systems.
Open Models in Closed Environments: The Sovereign AI Playbook
The Palantir-NVIDIA partnership reveals the emerging blueprint for sovereign AI: open-source models deployed inside closed government infrastructure.
The Sovereign AI Movement: Why Governments Are Building Their Own AI — And Why It Matters
Five European nations are building sovereign AI foundation models. This isn't about nationalism — it's about control. Here's what the movement means for government AI strategy worldwide.
Rampart and the Rise of Sovereign AI: Why Governments Are Building Their Own Models
The US government just open-sourced its first AI model. Rampart is 14.7 MB, runs locally, and signals a fundamental shift in how governments approach AI infrastructure.
The Open-Source Model Explosion Is Rewriting Enterprise AI Strategy
A food delivery company built a frontier AI model. Export controls pulled another offline. The enterprise takeaway: own your infrastructure or lose access to it.
The Fable 5 Blackout Proved Universities Need LLM-Agnostic AI Infrastructure
When the US government restricted Fable 5 and limited Mythos 5 to 100 organizations, universities locked into single-vendor AI learned the cost of dependency. Here is why LLM-agnostic infrastructure is now a strategic imperative for higher education.
K-12 AI: Unify District Data With an Ontology
K-12 AI agents fail when student data is scattered across the SIS, LMS, assessment, and special-education systems. The prerequisite is an ontology — a governed knowledge graph the district owns and self-hosts — that unifies those silos before any agent is deployed.
Higher Education AI: Unify Campus Data With an Ontology
Higher-ed AI agents fail when student data is scattered across the SIS, LMS, CRM, and financial aid systems. The prerequisite is an ontology — a governed knowledge graph the institution owns and self-hosts — that unifies those silos before any agent is deployed.
The Custom Silicon Race Signals Enterprise AI's Next Phase
Enterprise AI spending has shifted from training to inference. Custom silicon startups are racing to capture this market — and the implications for enterprise AI strategy are profound.
Enterprise AI Data Integration: The Ontology-First Approach
Enterprise AI agents fail when employee, customer, and operational data is scattered across CRM, HRIS, ERP, ITSM, and the data warehouse. The fix is an ontology — a governed knowledge graph the company owns and self-hosts — that unifies those silos before any agent ships.
The Karpathy Lesson for K-12: Teach Comprehension, Not Just Usage
Andrej Karpathy coined vibe coding, then stopped using AI for his most important work. His reasoning holds a critical lesson for how K-12 schools should teach AI.
De Facto AI Regulation Is Here — What Government Agencies Should Do Next
The White House is inserting itself between AI development and deployment. Government agencies need sovereign infrastructure that works regardless of which models are available.
IBM NanoStack: What Sub-1nm Chips Mean for Enterprise AI
IBM unveiled the first sub-1nm chip architecture. Here is what it means for enterprise AI infrastructure costs and deployment.
The Fable 5 Shutdown Changed Enterprise AI Forever
The US government's first-ever AI export control order pulled Anthropic's Fable 5 offline globally. Here's what every enterprise should learn from it.
Why AI Agents Fail Without an Ontology: Unify Data First
Most enterprise AI agents fail for one reason: organizational data is trapped in silos — SIS, LMS, CRM, ERP, HRIS. The fix isn't a better model. It's an ontology — a governed knowledge graph you own — built first, with agents deployed on top. Why data unification comes before automation.
Why 94% of Government AI Pilots Stall — And What Sovereign Infrastructure Changes
New research shows only 6% of organizations have deployed AI to production. Government agencies face even steeper odds — but sovereign AI infrastructure built on ownership, not licensing, is closing the gap.