You find a promising self-hosted LLM tool on Reddit or GitHub. The README says "minimal RAM requirements." You spin up a server, download 30GB of model weights, and hit an out-of-memory error three hours later. The tool works—just not with the specs you read.
This happens because marketing copy, GitHub READMEs, and third-party tool lists often omit or downplay real hardware constraints. Before you commit time and infrastructure to a local LLM stack, you need a verification workflow that separates marketing from reality.
A single tool's RAM requirement can be listed as "4GB minimum" on its website but "16GB recommended for usable inference" in GitHub issues. The first is technically true (the tool may load with 4GB). The second reflects what actually works. Marketing pages emphasize the lower number. Community lists copy the marketing number without testing. You're left guessing which one matters for your use case.
The same pattern repeats for GPU support, offline capability, and inference speed. A tool claiming "offline" may still phone home for telemetry. One claiming GPU acceleration may require a specific CUDA version your hardware doesn't have. These aren't malicious omissions—they're just marketing selectivity.
Start with the tool's official GitHub repository. Read the README, then look at open and closed issues. Search for your hardware configuration explicitly: "16GB RAM," your GPU model, your OS. People often post real results in issues: "worked fine on 8GB but OOM on 4GB," or "NVIDIA driver 515 required, 520 breaks inference."
Check the repository's discussions tab or linked Discord. Maintainers often clarify requirements there more candidly than in public docs. Look at the tool's recent releases and commit frequency. A tool with no commits in 18 months may work, but critical bugs and incompatibilities with newer libraries won't be fixed.
For vector databases and RAG frameworks, verify offline capability by examining their dependency trees. Does it import any external API clients? Search the codebase for hardcoded URLs or API key validation logic. Read the licensing—some "open source" tools have commercial restrictions on production use.
Step 1: Collect Claims and Sources
Write down every spec claim you find (RAM, GPU, offline, license). Note where you found it. Don't assume consistency. A tool's website may say "4GB" while its GitHub issues show users running it on 8GB minimum.
Step 2: Find the Source of Truth
For each claim, trace back to the original source. LICENSE file for license type. The tool's own repo or official docs for requirements. GitHub issues for real-world results. If you see a claim repeated across five blog posts but only as a screenshot, go back to the original tool and verify it there.
Step 3: Test on Your Hardware
Before deploying to production, run the tool on your actual target hardware for at least one inference cycle. Capture baseline memory and GPU usage. This takes an hour and saves you from a failed rollout later.
If a tool's README lists only "minimum" requirements with no mention of real-world constraints, dig into issues. If specs vary across versions and the docs don't mention which version they apply to, test your intended version explicitly. If offline capability is claimed but the tool requires internet for model downloads or authentication, that's offline-for-inference only—clarify this before deployment.
If the last commit was more than a year ago, test compatibility with your current OS, Python version, and any GPU drivers. Old tools often break silently on newer systems.
Building a reliable local LLM stack means accepting that you'll spend 2–3 hours per tool verifying specs. This overhead pays back immediately once you avoid one failed deployment.