What’s the Best Open-Source AI Video Model You Can Actually Run? (8GB → 24GB VRAM)

As of mid-2026, open-source AI video models like Wan 2.2, Open-Sora 2.0, and HunyuanVideo can run on consumer GPUs with 8GB to 24GB VRAM, generating 5-20 second clips at 720p or 1080p. However, leaderboards and aggregator sites often misrepresent VRAM requirements and model performance, making it difficult to choose the best self-hosted option versus commercial services like Runway.

Member-only story What’s the Best Open-Source AI Video Model You Can Actually Run? 8GB → 24GB VRAM The honest case for self-hosting in 2026 — and when paying Runway still wins. Read the article for free . here The graphics card in your desk is doing a lot less than it could. Most of the time it sits idle. When you do use it, you’re running games or maybe a local language model. As of mid-2026, that same card can also generate video — five to twenty seconds at a time, in 720p or 1080p depending on which model you pick and how patient you are with render times. Search ‘ best open-source AI video model 2026 ’ and the first six results disagree on which model is best, which numbers to trust, and which ones will fit in 12GB of VRAM. The landscape is louder than it is honest The current leaderboards and aggregator sites paint a picture of an open-source ecosystem that has already surpassed commercial APIs. Their headline numbers: Wan 2.2 at 84.7% on VBench, Open-Sora 2.0 closing the Sora gap to 0.69%, HunyuanVideo’s 96.4% visual quality on a 4090. These aggregators scrape GitHub READMEs, mix up model versions, hallucinate VRAM requirements, and present it all as a buying guide. When…