The increasing corporate adoption of agentic workflows is acting as a significant cost multiplier for AI usage.

The AI industry is becoming increasingly volatile due to geopolitical forces.

The potential for governments to shut down major AI vendors creates a significant dependency risk for businesses relying on a single provider.

While running local AI models can save on token costs, it introduces significant expenses for hardware, maintenance, updates, and specialized personnel.

A shortage in memory is causing the price of AI-capable hardware to increase.

For technically capable teams, self-hosting AI models on rented cloud GPUs can offer the lowest per-query cost at high volumes.

Most large enterprises are initially adopting open-source models by running them on existing cloud platforms like AWS Bedrock, Google Vertex, or Azure AI Foundry.

The starting cost for an enterprise-grade server for a data center to run AI models is approximately $250,000.

The amount of VRAM on a GPU is the most critical factor for running AI models, as the entire model needs to fit in this memory for usable speed.

The GGUF file format is used for compressed, ready-to-run versions of AI models that can be downloaded for local deployment.

A locally hosted machine connected to the internet is not inherently more secure than a well-configured cloud API from a major provider.

The Q4 quantization level is a standard default that compresses an AI model to approximately 30% of its original size, making it suitable for most local deployment tasks.

Small, specialized models like ViveThinker achieve high performance on structured, verifiable tasks like coding, but not on general knowledge work.

Open Web UI is a popular self-hosted web application that provides a ChatGPT-like interface for interacting with locally served models via Ollama.

The cost of human capital required to maintain a local AI deployment can often outweigh the savings on token costs from cloud providers.

The OpenRouter service provides a single interface to connect to over 400 AI models from more than 60 providers.

The unified memory architecture of Apple Silicon, where the CPU and GPU share the same memory pool, allows Mac computers to run large AI models, making them popular for local AI development.

The release of Anthropic's Opus 4.7 model, which included a tokenizer change, caused some companies' bills to increase by as much as 35% without any changes to their prompts.

Ollama is the most popular open-source tool for serving AI models on a local machine.

Ollama exposes a standard API, allowing tools designed for cloud-based AI services to be redirected to a locally running model with minimal changes.