The Full Toolkit

You’re comfortable with AI. Here’s everything VT gives you to work with.

If you landed here without going through the earlier pages, that’s fine — the previous tiers cover the everyday tools (Gemini, ARC web chat, Copilot) and the next-step tools (Copilot in Office, NotebookLM, document upload, the AI Pro upgrade). This page focuses on the things you only need if you want to run AI locally on your own machine, build your own tools against an API, or wire AI into your development environment.

Most people in AAD never need anything on this page. If you’re curious anyway, read on — nothing here is mandatory and nothing here will break your computer.

Already Covered

Quick reference for tools from the previous tiers — follow the links for full details.

Google Gemini — Chat, summarize, Gemini Live voice mode (Flash model on standard VT accounts; higher-end models via AI Pro add-on). Getting Started
VT ARC LLM (llm.arc.vt.edu) — Browser-based chat, image gen/analysis, VT-hosted and ITSO-approved. Getting Started
Microsoft Copilot (free) — Standalone web chat, no Office integration. Getting Started
Microsoft Copilot (paid) — Copilot in Word, Excel, PowerPoint, Outlook, Teams; Copilot Studio. $216/year. Doing More
ARC Document Upload — Upload a PDF or doc to ARC and ask questions across it. Private, on VT servers. Doing More
Google NotebookLM — Multiple sources, AI summaries, audio overviews. Doing More
Groq Cloud — Fast open source model testing. Third-party — no sensitive data. Doing More
HuggingFace Chat — Try dozens of open source models. Third-party — no sensitive data. Doing More
Gemini AI Pro for Education — 1M context, 20 Deep Research reports/day, Docs/Drive integration, full Gemini Live on higher-end models. ~$15–$24/user/month. Doing More

Ollama — Run Models Locally

Download from ollama.com, install it, pull a model, and run it. That’s it.

ollama pull llama3.2
ollama run llama3.2

100% offline. Nothing leaves your machine. 16GB RAM is the practical baseline — most AAD-managed machines qualify.

ARC already gives you open source models in a browser. Ollama is for when you want offline access, a wider model selection, or full control over which AI you use and how it runs.

Your first Ollama session, step by step:

Download from ollama.com and run the installer. On Mac, the installer drops an icon in your menu bar that means it’s running in the background.
Open Terminal (Mac) or PowerShell (Windows).
Type ollama pull llama3.2 and hit enter. This downloads a model — around 2 GB. Grab a coffee.
Type ollama run llama3.2 and hit enter. You’re now chatting with a model running entirely on your machine — no internet required after this point.
Type /bye when you’re done.

If 2 GB feels like a lot, try llama3.2:1b instead — it’s smaller and faster, and still good enough for most chat tasks. If you have a powerful machine and want something bigger, browse the model library on ollama.com.

ARC API Access

ARC runs an OpenAI-compatible endpoint. It’s free to use with your VT credentials.

Get your API key: llm.arc.vt.edu > Settings > Account > API Keys

Base URL: https://llm-api.arc.vt.edu/api/v1/

Rate limits: 60 req/min · 1,000/hr · 3,000/3hr (API). Web interface: 2,000/3hr.

Any tool or library that accepts an OpenAI-compatible endpoint will work — swap in the base URL and your ARC key. From there you can build custom scripts, document processing pipelines, research automation, whatever you need.

Full API docs and examples: docs.arc.vt.edu

What does this actually let you do? A few practical examples:

A script that takes a folder of meeting notes and produces a one-page summary of each
A custom chatbot for your department’s policies, hosted internally
A nightly process that reads through new tickets and tags them by topic
A research tool that pulls structured data out of free-form survey responses

If you’ve never written code against an API before, the ARC docs have copy-paste starter examples in Python and JavaScript. You don’t need to be a developer — if you can edit a script and run it from a terminal, you can use this. If you’d rather have someone help you scope a project, contact AAD IT.

IDE Integration

ARC’s API plugs into your IDE as an OpenAI-compatible backend.

VS Code: GitHub Copilot Chat extension, Insiders v1.104+. Point it at the ARC API endpoint instead of OpenAI’s. Full setup at docs.arc.vt.edu/ai/040_ides.html.

IntelliJ IDEA: Native AI plugin, v2025.3.2+. Same deal — configure the endpoint URL and your API key.

Config notes:

Endpoint: https://llm-api.arc.vt.edu/api/v1/
Model names rotate — check the ARC portal for current available models
Token limits vary by model (example: 65,536 input / 32,768 output for some current models)

The ARC docs have current model names and full config walkthroughs.

Local, cloud, or API — which should I use?

Three ways to interact with these models, each with tradeoffs:

ARC web interface (llm.arc.vt.edu). The easiest by far. Browser, VT login, done. Use this for most day-to-day work.
Ollama (local). Runs on your machine. More limited model selection (depending on your hardware), but completely offline. Use this when you want to experiment without internet, or when you’re somewhere without reliable connectivity.
ARC API. For when you want to build something — a script, a tool, an integration. Same models as the web interface, accessed programmatically. Use this when you’ve outgrown copy-pasting into a chat window and you want to automate something.

Most people never need anything beyond the ARC web interface. The other two are there for when the web interface stops being enough.

Open Source Model Landscape

There are hundreds of open source models. They vary wildly in capability, size, and what they’re good at. A few things worth knowing:

Hugging Face is the central hub. Model cards, leaderboards, and the HuggingFace Chat interface (which lets you test models in a browser without installing anything) all live there.

For a solid 17-minute orientation to the different ways to run open source models, Tina Huang’s video is worth your time: Every Way To Run Open Source AI Models.

The landscape changes fast. Models rotate on ARC, new ones drop monthly, benchmarks shift. Don’t get attached to specific model names — treat ARC and Ollama as services, not as “the model I use.”

Questions? Contact AAD IT: aadithelp@vt.edu

Last updated: April 2026