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A practical 2026 guide to LLM gateways and FinOps: request taxonomy, model routing, token budgets, caching caveats, failover, multi-provider contracts, chargeback, security, observability, and a 90-day rollout plan.
OpenAI’s National Science Initiative funds model access for labs and universities, but validation, governance, and expert review remain decisive.
Moonshot AI has released Kimi K3 weights. Here is what its MoE design, 1.4 TB footprint, and licence mean before adoption.
How to select a language model for enterprise AI using scenarios, security, cost, and infrastructure requirements.
Eight practical lessons for running open LLMs: inference control, task-specific model selection, prompt templates, and structured output.
Colibri runs the GLM-5.2 MoE model without a GPU by keeping dense weights in RAM and streaming experts from NVMe. Here are the trade-offs.
Hunter Alpha was an early MiMo-V2-Pro build. Its uptake shows how price, output quality, and data jurisdiction shape model selection.
How the limits of search and rule-based systems led to LLMs—and why a model is an interface to knowledge, not enterprise memory.
A practical Claude Desktop-inspired setup: DuckDB performs calculations while the model works through schemas, tools, and guarded access.
A home chatbot experiment: fine-tune a tiny local LLM with Unsloth and two-letter category codes instead of label names for metadata-aware RAG.