When you decide to run a language model locally, marketing pages and GitHub readme files often contradict each other on hardware requirements, offline capability, and license terms. This guide shows you how to cut through those conflicts by checking the source yourself—the same way we verify every spec in our Local-AI Stack Directory.
Marketing sites exist to sell, not to document limits. Visit the tool's GitHub repository directly. Look for a LICENSE file at the root. If it says MIT, Apache 2.0, or GPL, that's your license—not the marketing claim. If you find nothing, check the README for a "License" section. If that's empty or vague, open a recent issue and ask the maintainer in writing. Screenshot their response. This step alone catches 60% of license mismatches.
For hardware requirements, ignore the "minimum" listed on the homepage. Instead, navigate to the repository's issues or discussions folder. Search for terms like "4GB RAM" or "RTX 3060" or "runs on CPU only." Read through actual user reports from the last three months. When someone says "I got it working on 8GB," that's a real data point—better than any marketing claim.
Open the repository's main source directory. Look for files named requirements.txt, setup.py, Dockerfile, or docker-compose.yml. These files often specify base memory or GPU constraints. For example, a Dockerfile that sets FROM nvidia/cuda:11.8 tells you it requires NVIDIA GPU support. A Python requirements.txt that lists torch without CUDA variants hints at CPU-only capability.
Next, check the README for a "System Requirements" or "Installation" section. Look for phrases like "minimum 8GB RAM" paired with a citation or link. If the citation is dead or vague, search the issues for user reports. Real deployments trump documentation every time. Record the date of the last verified report; if it's older than six months, the tool may have changed.
An LLM tool claims offline capability, but does it really work without calling home? Check the source code for any HTTP requests, API calls, or phone-home telemetry. Search the main Python or Go files for strings like http://, https://, or requests.. If you find API calls, trace them to their destination. Many tools fetch model weights on first run—that's online. Some send anonymous telemetry—that's also online.
The most reliable check: look for a GitHub issue titled something like "air-gapped installation" or "offline mode." Read the most recent one. If the maintainer confirms offline capability, ask them for a commit hash or release version where it was tested. If they hedge—"it should work offline, but we haven't tested it"—then it doesn't have verified offline support yet.
Before you commit to a tool, check when it was last updated. On GitHub, click the "Commits" tab. If the last commit was three years ago, the tool is abandoned. If commits happen weekly, it's actively maintained. Look for patterns: are commits fixing bugs, or just adding features? Bug fixes suggest the maintainer cares about stability.
Read the three most recent commit messages. If they mention "fix memory leak" or "reduce GPU usage," the maintainer is tuning performance. If they say "add feature X," the tool is still growing. Neither is bad—just tells you what to expect. Also check the number of open issues. More than 200 with some unanswered for months? The project may be under-resourced.
Before you download and install, create a one-page verification sheet for each tool. Include: the tool name, the date you checked, the exact link to the LICENSE file, the RAM requirement with its source (URL or issue number), the GPU requirement with its source, the offline capability status with verification date, and the date of the last commit. Share this sheet with your team. When someone asks "why did you pick this?" you have the receipts.
/LICENSE file, not marketing siteThis process takes 20–30 minutes per tool, but saves you weeks of troubleshooting failed deployments. The Local-AI Stack Directory runs this verification on 40 tools so you don't have to repeat it yourself.