Best Open-Source AI Video Generators 2026: 6 Options Compared

Open-source AI video generators let you make clips on your own hardware, with no credits and no queue, but the hardware bill is the catch. Several models in this list need 50 to 60 GB of GPU memory in their published setups, which means a data-center card or a rented cloud GPU, not a gaming laptop. Smaller options exist: Wan 2.1 has a 1.3B text-to-video model that needs roughly 8 GB of VRAM, LTX-Video has community 8-bit builds for smaller cards, and CogVideoX lists figures of about 4 to 10 GB with optimizations switched on. ComfyUI is not a model but the node-based interface most people use to run these models locally, and it works on Windows, macOS and Linux. Licenses deserve a read: several repositories use Apache-2.0 for code while certain model weights, such as the larger CogVideoX, carry their own terms. Expect shorter clips and rougher motion than the best paid services, plus setup time spent on Python, drivers and model downloads. The reward is control: no content filters from a vendor, no per-clip charge, and files that never leave your computer. Check each README before buying hardware, as requirements shift with every release.

Wan 2.1

Apache-2.0 models for self-hosting on NVIDIA GPUs. The smallest text-to-video 1.3B model needs about 8.19 GB of VRAM, the most approachable entry here. Newer Wan releases were not checked.

Price: Free (Free to run on your own hardware; the smallest T2V-1.3B model needs about 8.19GB VRAM) (Wan 2.1 pricing, checked 2026-10-22).

Platforms and license: Self-hosted (Python, NVIDIA GPU). Apache-2.0 (open source) (license source).

Project activity: 17.3k GitHub stars · last commit Mar 5, 2026

Best for: Consumer GPUs.

LTX-Video

Apache-2.0 repository, but some checkpoints are under OpenRAIL-M, so read the license of the version you download. Community 8-bit builds exist for smaller cards.

Price: Free (Free to run on your own hardware; check each checkpoint license for commercial use) (LTX-Video pricing, checked 2026-10-22).

Platforms and license: Self-hosted (Python; CUDA, or MPS on macOS). Apache-2.0 (repository); some checkpoints under OpenRAIL-M (open source) (license source).

Project activity: 11.2k GitHub stars · last commit Jan 5, 2026

Best for: Experimenting with checkpoints.

ComfyUI

GPL-3.0 node-based interface for local image and video models on Windows, macOS and Linux. Its README says big models can run on as little as 4 GB VRAM plus 8 GB RAM.

Price: Free (Free to run locally; Comfy Cloud is a separate paid hosted option) (ComfyUI pricing, checked 2026-10-22).

Platforms and license: Windows, macOS, Linux. GPL-3.0 (open source) (license source).

Project activity: 143.6k GitHub stars · latest release v0.43.0 (Oct 27, 2026) · last commit Oct 27, 2026

Best for: Running models through a visual workflow.

CogVideoX

Code is Apache-2.0 and the 2B weights are Apache-2.0, while the 5B weights use a separate CogVideoX license. README lists about 4 to 10 GB GPU memory with optimizations.

Price: Free (Free to run on your own hardware; 5B weights have their own license) (CogVideoX pricing, checked 2026-10-23).

Platforms and license: Self-hosted (Python, GPU). Apache-2.0 code; CogVideoX-2B weights Apache-2.0, 5B weights under a separate CogVideoX license (open source) (license source).

Project activity: 13.1k GitHub stars · latest release v1.0 (Nov 8, 2024) · last commit Nov 4, 2025

Best for: Smaller GPUs with license care.

Mochi 1

Apache-2.0 video model from Genmo. The README asks for about 60 GB of VRAM on a single GPU and recommends at least one H100, so plan for rented hardware.

Price: Free (Free to run on your own hardware; needs roughly 60 GB VRAM per the README) (Mochi 1 pricing, checked 2026-10-23).

Platforms and license: Self-hosted (Python, NVIDIA GPU). Apache-2.0 (open source) (license source).

Project activity: 3.8k GitHub stars · last commit Oct 22, 2026

Best for: High-end or rented GPUs.

Open-Sora

Apache-2.0 video generation project. Its benchmarks use H100 or H800 GPUs, with peak memory of about 52.5 GB at 256x256 and 60.3 GB at 768x768.

Price: Free (Free to run on your own hardware; benchmarks use 52 to 60 GB GPU memory) (Open-Sora pricing, checked 2026-10-23).

Platforms and license: Self-hosted (Python, GPU). Apache-2.0 (open source) (license source).

Project activity: 29.9k GitHub stars · latest release v1.3 (Feb 21, 2025) · last commit Apr 9, 2026

Best for: Research and high-memory setups.

Which one to pick

• You have a single consumer graphics card: Wan 2.1
• You want a visual workflow for models: ComfyUI
• You need commercial clarity on weights: CogVideoX
• You can rent an H100: Mochi 1
• You want to follow the research code: Open-Sora