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Run Open-Source AI Models Locally

Run Open-Source AI Models Locally

Open-Source Models

A practical guide to running open-weight AI models on your own hardware, including hardware requirements, quantization, and the real quality tradeoffs.

AP
Staff
September 2, 2026 · 1 min read

Running a model on your own machine used to require real ML infrastructure knowledge. It doesn’t anymore, and that’s changed who actually does it.

Ollama is the most common way in: install it, pull a model, run it, all from a few terminal commands. No separate Python environment to configure, no manual dependency wrangling.

Quantized models, compressed versions trading some precision for a much smaller footprint, run on consumer hardware that wouldn’t touch the full-precision version. A capable coding or writing model can run on a decent laptop, no dedicated GPU cluster required.

Local models trade some capability for real privacy, your data never leaves your machine, and for reliability, no API outage, no rate limit. They’re not going to match a hosted frontier model on the hardest reasoning tasks. For genuinely private or offline use cases, that tradeoff is often worth it.

Start with a smaller quantized model matched to your actual hardware before assuming you need the largest available version, our full guide to choosing open versus closed models covers the broader tradeoffs.

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AI Benchmark Scores Explained

AI Benchmark Scores Explained

Model Comparisons

A guide to reading AI benchmark scores critically, including benchmark saturation, why vendor-reported numbers need verification, and what to test instead.

AP
Staff
September 1, 2026 · 1 min read

Every model release comes with a wall of benchmark percentages. Here’s how to actually read them instead of trusting whichever number a company leads with.

A coding benchmark like Terminal-Bench measures a specific set of coding tasks, not general intelligence. A model scoring higher there says nothing about performance on a different task type. Check what a benchmark actually tests before treating a score as a general ranking.

Watch for saturation. Our coverage of computer-use agents on OSWorld 2.0 is the clearest example: scores climbed from roughly 12% to 85% in about two years, a suspiciously steep curve that turned out to mean the benchmark had become too easy, not that models had solved the underlying problem.

A company reporting its own benchmark results has an obvious incentive to present them favorably. Treat vendor-reported numbers as claims, not verified facts, until independent testing confirms them.

Test a model against your own task, not a demo, and track your real acceptance or edit rate. See Papers With Code for independently tracked leaderboards across most major benchmarks.

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