Wan 2.2 and LTX-2.3 now produce output that rivals Sora and Veo on motion realism, prompt adherence, and clip quality. Choosing between them requires weighing VRAM floors, license terms, audio support, and architectural tradeoffs.
This guide covers the five best open-source AI video generation models in 2026, with hardware requirements, license details, and GPU pricing to help you pick the right one.
Takeaways
- LTX-2.3 is the only open-source model with native audio: generates synchronized audio and video in a single diffusion pass, with 4K output at 50fps.
- Wan 2.2 is the safest default: Apache 2.0 licensed, runs from 8GB VRAM on the 5B GGUF with memory offloading.
- HunyuanVideo 1.5 runs on a single RTX 4090: from ~14GB VRAM with FP8 and CPU offloading.
- CogVideoX-5B leads on prompt adherence: produces better semantic accuracy for structured, multi-clause prompts.
- Mochi 1 is the most accessible fine-tuning base: the official LoRA trainer supports single-GPU operation and produces fluid secondary motion at 480p.
What Makes a Video Model "Open Source"?
Most models described as open source are open-weight: weights are public, but training data and pipeline code may stay proprietary. A fully open model publishes all four under a permissive license.
For most developers, open source means three things: downloadable weights, local inference, and commercially usable output.
License Types at a Glance
| License | Commercial Use | Notable Models |
|---|---|---|
| Apache 2.0 | Unrestricted | Wan 2.2, Mochi 1, CogVideoX-2B |
| LTX-2 Community / Commercial License | Free for non-commercial and organizations under $10M revenue; commercial license required above that threshold | LTX-2.3 |
| Tencent Community License | Free up to 100M monthly active users; license required above that threshold | HunyuanVideo 1.5 |
| CogVideoX License | Commercial use permitted with restrictions on prohibited applications | CogVideoX-5B |
| Verify the current license before deploying commercially, as terms are updated frequently. | ||
How to Evaluate Video Generation Models
Output Quality and Motion Realism
Wan 2.2 and HunyuanVideo 1.5 lead on photoreal output for faces and dynamic scenes. Models built on Diffusion Transformer (DiT) architectures generally outperform earlier U-Net approaches on motion coherence. LTX-2.3 produces strong results at 4K with the added advantage of synchronized audio.
Inference Speed and Generation Time
Generation time scales with model size, quantization level, resolution, and clip length. A 5-second 480p clip on an RTX 4090 takes roughly 4–6 minutes with Wan 2.2's 5B variant. Speed matters most for iterative prompt testing; for batch production, cost/clip is the more relevant metric.
VRAM Requirements
VRAM is the primary hardware constraint for self-hosted video generation. Video models need more memory than image models because they maintain temporal coherence across frames, not just spatial resolution.
Requirements range from 8GB for Wan 2.2's 5B GGUF variant to 80GB for LTX-2.3 at full precision. FP8 and GGUF quantization reduce VRAM usage at a modest quality cost.
Fine-Tuning and LoRA Support
Fine-tuning is essential for consistent character appearance, branded aesthetics, and domain-specific content. LTX-2.3 offers IC-LoRA adapters with pose, depth, and edge map conditioning. Wan 2.2 has a growing library of community LoRAs on CivitAI. CogVideoX supports fine-tuning through the CogKit framework.
Mochi 1 provides an official LoRA training script that runs on a single GPU, making it the most accessible fine-tuning base for teams without multi-GPU infrastructure.
Audio Generation
LTX-2.3 generates synchronized audio in a single diffusion pass. All other models require a separate audio generation step in post-production, which adds pipeline complexity and introduces sync drift.
The Best Open-Source AI Video Generation Models in 2026
| Model | Variant / Precision | Min VRAM | Max Resolution | Native Audio | License | Best For |
|---|---|---|---|---|---|---|
| Wan 2.2 | TI2V-5B (GGUF) | 8GB | 720p | No | Apache 2.0 | Overall versatility, commercial use, consumer GPUs |
| A14B MoE (GGUF) | 12GB | |||||
| TI2V-5B (BF16) | 24GB | |||||
| LTX-2.3 | Distilled (NVFP4, Blackwell) | 16GB | 4K @ 50fps | Yes | LTX-2 Community / Commercial | Native audio, local 4K, ComfyUI pipelines |
| Distilled (FP8) | 32GB | |||||
| Dev (BF16) | 80GB | |||||
| HunyuanVideo 1.5 | FP8 + CPU text-encoder offload | 14GB | 1080p | No | Tencent Community License | Cinematic motion, consumer GPU access |
| FP16 | 24GB | |||||
| CogVideoX | 2B (FP16) | 16GB | 720p | No | Apache 2.0 | Prompt adherence, low hardware floor |
| 5B (BF16) | 24GB | CogVideoX License | ||||
| Mochi 1 | FP8 (ComfyUI) | 20GB | 480p | No | Apache 2.0 | Smooth motion, fine-tuning base |
| BF16 | 60GB |
Wan 2.2: Most Versatile
Wan 2.2 is a widely deployed open-source video model in 2026, released by Alibaba's Tongyi Lab on July 29, 2025. Its Mixture-of-Experts (MoE) design routes denoising work between specialized high-noise and low-noise expert networks, producing sharper detail without a proportional increase in compute cost.
Wan 2.2 ships in two practical tiers:
- TI2V-5B supports text-to-video and image-to-video at 720p on 24GB consumer GPUs.
- A14B MoE has 27B total parameters with 14B active per step, runs on 40–48GB cards, and produces higher quality output.
Full guide: How to run Wan 2.2 with Thunder Compute's one-click ComfyUI template
LTX-2.3: Native Audio and 4K
LTX-2.3 is an open-source video model that generates synchronized audio and video in a single inference pass, released by Lightricks on March 5, 2026. The 22B-parameter architecture combines a 14B video stream with a 5B audio stream across 48 shared transformer blocks.
The pipeline supports text-to-video, image-to-video, audio-to-video, IC-LoRA conditioning with pose and depth control, and a distilled fast-inference variant. Maximum output is 4K at 50fps for clips up to 20 seconds. ComfyUI support is native from v0.16.1, with day-0 reference workflows for all major pipeline variants.
HunyuanVideo 1.5: Best Motion Quality
HunyuanVideo 1.5 is Tencent's consumer-accessible version of their flagship video model, going from 13B to 8.3B parameters while maintaining competitive output quality. With FP8 quantization and CPU offloading of the text encoder, HunyuanVideo 1.5 runs on ~14GB VRAM, making it practical on a single RTX 4090 or RTX 4080 Super.
HunyuanVideo 1.5 generates text-to-video and image-to-video clips at up to 1080p, with strong community reception for motion physics, lighting transitions, and facial realism. Community GGUF builds lower the hardware floor to 8–12GB.
HunyuanVideo 1.5 is released under the Tencent Community License, allowing commercial use below 100M monthly active users. But fine-tuning support is limited.
CogVideoX-5B: Best Prompt Adherence
Developed by Zhipu AI and Tsinghua University's THUDM lab, CogVideoX is great for translating detailed, multi-clause prompts into accurate video. Its expert transformer architecture processes text and visual tokens in a unified space rather than conditioning on text embeddings separately. This results in better semantic accuracy when prompts specify object positions, interactions, or sequential actions.
The 2B version is Apache 2.0 licensed and runs on 16GB GPUs. The 5B variant produces better output on 24GB but uses the CogVideoX License. Both support text-to-video and image-to-video at up to 720p, with fine-tuning available through the CogKit framework.
Mochi 1: Best for Fine-Tuning and Smooth Motion
Mochi 1 is a 10B-parameter model built on the Asymmetric Diffusion Transformer (AsymmDiT) architecture, developed by Genmo and released under Apache 2.0. Different attention configurations for conditioning and generation streams produce fluid motion dynamics, particularly for secondary motion like hair, fabric, and water.
Output is capped at 480p, making Mochi 1 a poor fit for HD or 4K production work. Its value is as a fine-tuning base and standalone model for smooth short clips. Genmo ships an official LoRA trainer that supports single-GPU fine-tuning, making it the most accessible fine-tuning foundation in the open-source video space.
GPU Requirements for Running Open Source Video Generation Models
| Model | Minimum GPU | Recommended GPU | Thunder Compute Price | Notes |
|---|---|---|---|---|
| Wan 2.2 TI2V-5B | RTX 4090 (24GB) | RTX A6000 (48GB) | From $0.35/hr | 5B runs on RTX 4090; A14B needs 40–48GB |
| LTX-2.3 | RTX A6000 (48GB) | A100 80GB | From $1.09/hr | FP8 distilled runs on 32GB; BF16 needs 80GB |
| HunyuanVideo 1.5 | RTX 4090 (24GB) | RTX A6000 (48GB) | From $0.35/hr | FP8 + CPU offloading runs on 14GB; 24GB comfortable |
| CogVideoX-5B | RTX 4090 (24GB) | RTX A6000 (48GB) | From $0.35/hr | 2B variant runs on 16GB; 5B comfortable on 24GB |
| Mochi 1 | RTX A6000 (48GB) | A100 80GB | From $0.35/hr | ~20GB at FP8; 80GB required at full precision for fine-tuning |
Self-hosting becomes cost-effective at scale. At hundreds of clips/day, the margin over API pricing compounds quickly and marginal cost per clip gets lower.
Running Video Generation Models on Thunder Compute
Thunder Compute can run all five models using ComfyUI, a node-based interface that handles model loading, prompt routing, and output management without code. The one-click ComfyUI template launches a running instance on an RTX A6000 48GB at $0.35/hr.
The RTX A6000 48GB covers most open-source video generation workloads: Wan 2.2 A14B, HunyuanVideo 1.5, and CogVideoX-5B all run at full quality without memory pressure. For LTX-2.3 at full BF16 precision or Wan 2.2 at the highest resolution settings, the A100 80GB provides the headroom those workloads need.
For GPU recommendations specific to video generation workloads, see the full guide to the best cloud GPUs for AI generation.
Last Thoughts on Open-Source AI Video Generation Models
Wan 2.2 leads on versatility and commercial accessibility, LTX-2.3 on native audio and 4K output, HunyuanVideo 1.5 on cinematic motion quality, CogVideoX-5B on prompt adherence, and Mochi 1 on fine-tuning support. Pick the model that fits your output requirements, verify the license, and match it to the right GPU tier.
FAQ
What is the best open-source AI video generation model in 2026?
Wan 2.2 is the most versatile choice: Apache 2.0 licensed, with a 5B variant that runs on 8GB VRAM. LTX-2.3 leads on native audio-video generation and local 4K output. The right model depends on your VRAM budget, audio needs, and licensing requirements.
What VRAM do I need to run open-source video generation models?
Wan 2.2's 5B GGUF variant runs on 8GB. HunyuanVideo 1.5 needs ~14GB with FP8 and CPU offloading. CogVideoX-5B runs on 24GB; its 2B variant on 16GB. LTX-2.3 requires 32GB distilled or 80GB at full precision. Mochi 1 runs on ~20GB at FP8.
Which open-source video model supports audio generation?
LTX-2.3 is the only open-source video model in 2026 that generates synchronized audio and video in a single diffusion pass. All other models generate video only; audio must be added separately in post-production.
Can I run open-source video generation models on a cloud GPU?
Yes. Thunder Compute offers ComfyUI instances for Wan 2.2 from $0.35/hr on an RTX A6000 48GB. A Wan 2.2 A14B clip at 480p costs ~$0.02–$0.03, significantly cheaper than API-based video generation at scale.
What is the best open-source video model for fine-tuning?
Mochi 1 is the most accessible fine-tuning base, with an official LoRA trainer that supports single-GPU operation. LTX-2.3 supports IC-LoRA adapters with camera, pose, and depth control. CogVideoX supports fine-tuning through the CogKit framework.
Can I use open-source video generation models commercially?
It depends on the model. Wan 2.2, Mochi 1, and CogVideoX-2B are Apache 2.0 with no commercial restrictions. LTX-2.3 requires a commercial license above $10M revenue. HunyuanVideo 1.5 permits commercial use below 100M monthly active users. Always verify terms before deploying.