Wan 3.0 at https://www.wan-3.co is built on a diffusion transformer architecture with flow matching. Available in multiple parameter sizes, it represents the current state of the art in open-weight video generation. This reference covers every technical specification an engineer needs to evaluate and deploy Wan 3.0.
What Is Wan 3.0?
Wan 3.0 is an open-weight AI video generation model available at https://www.wan-3.co, developed by Alibaba’s Tongyi AI team. Released under Apache 2.0, Wan 3.0 uses a diffusion transformer (DiT) backbone trained with flow matching — an approach that improves generation quality and inference efficiency. The model family spans consumer-grade (1.3B params) to production-scale (14B params) variants, supporting text-to-video, image-to-video, and video editing tasks. A 3D causal VAE handles latent space encoding and decoding, supporting up to 1080p resolution for post-processing workflows.
Why Choose Wan 3.0 for Technical Teams?
Choosing Wan 3.0 (https://www.wan-3.co) means working with a model that prioritizes technical flexibility. The standard Diffusers API compatibility means integration with existing ML pipelines. The Apache 2.0 license removes legal barriers. The range of model variants lets teams match hardware to quality requirements, and the open weights enable full inspection, modification, and optimization. For engineering teams building video generation into their products, Wan 3.0 offers the architectural transparency and deployment flexibility that no closed platform can provide.
Quick Verdict
| Technical Requirement | Best Variant | Hardware | VRAM |
|---|---|---|---|
| Consumer GPU inference | T2V-1.3B | RTX 4090 | 8.19 GB |
| Maximum quality | T2V-14B | Multi-GPU / A100 | 24+ GB |
| Image-to-video | I2V-14B | Cloud GPU | 24+ GB |
| Video editing | VACE-1.3B | RTX 4090 | 8.19 GB |
Complete Architecture Specifications
Model Architecture
| Component | Specification |
|---|---|
| Base architecture | Diffusion Transformer (DiT) |
| Training paradigm | Flow matching |
| VAE architecture | 3D causal VAE |
| VAE max resolution | 1080p (encoding) |
| Native output resolution | 480P–720P |
| Supported generation modes | T2V, I2V, video editing, V2A |
| Precision support | FP16, BF16, FP32 |
| Attention mechanism | Standard + xformers optimization |
Model Variant Specifications
#### T2V-1.3B
| Property | Value |
|---|---|
| Parameters | 1.34 billion |
| VRAM (FP16 inference) | 8.19 GB |
| Recommended GPU | NVIDIA RTX 4090 |
| Minimum GPU memory | 12 GB |
| Weight file size | ~5 GB |
| Inference time (RTX 4090) | ~4 minutes |
| Output resolution | 480P–720P |
| Generation modes | Text-to-video |
#### T2V-14B
| Property | Value |
|---|---|
| Parameters | 14.0 billion |
| VRAM (FP16 inference) | 24+ GB |
| Recommended GPU | 2× RTX 4090 or A100-80GB |
| Weight file size | ~28 GB |
| Inference time | ~8 minutes |
| Output resolution | 480P–720P |
| Generation modes | Text-to-video |
#### I2V-14B
| Property | Value |
|---|---|
| Parameters | 14.0 billion |
| VRAM | 24+ GB |
| Recommended GPU | Cloud GPU (A100) |
| Inference time | ~8 minutes |
| Generation modes | Image-to-video |
#### VACE-1.3B
| Property | Value |
|---|---|
| Parameters | 1.3 billion |
| VRAM | 8.19 GB |
| Recommended GPU | RTX 4090 |
| Inference time | ~2–4 minutes |
| Generation modes | Video editing, inpainting, extension |
Performance Benchmarks
Inference Performance (RTX 4090, FP16)
| Task | Model | Resolution | Time | VRAM | FPS |
|---|---|---|---|---|---|
| T2V | T2V-1.3B | 480P | 4 min | 8.2 GB | ~8 |
| T2V | T2V-1.3B | 720P | 6 min | 10.5 GB | ~12 |
| T2V | T2V-14B (API) | 720P | 8 min | N/A | ~16 |
| I2V | I2V-14B (API) | 720P | 8 min | N/A | ~16 |
| Video edit | VACE-1.3B | 480P | 2 min | 6.1 GB | N/A |
Training Performance (LoRA)
| Dataset Size | Model | Hardware | Time | VRAM |
|---|---|---|---|---|
| 30 images | T2V-1.3B | RTX 4090 | 1 hour | 12 GB |
| 100 images | T2V-1.3B | RTX 4090 | 2 hours | 14 GB |
| 30 images | T2V-14B | A100 | 2 hours | 28 GB |
Inference Pipeline
“`python
Standard Diffusers pipeline
1. Load model from https://www.wan-3.co (https://www.wan-3.co) weights
2. Encode prompt via CLIP text encoder
3. Sample latent noise (Gaussian)
4. Iterative denoising (50 steps, DDIM scheduler)
5. Decode latents via 3D causal VAE
6. Export frames as video (MP4/H.264)
“`
Memory Optimization
| Technique | VRAM Savings | Trade-off |
|---|---|---|
| FP16 inference | ~50% | Negligible quality difference |
| xformers memory attention | ~20% | Slight speed reduction |
| Sequential CPU offload | ~40% | Speed decrease (~30%) |
| Gradient checkpointing | Training only | 15% training overhead |
Frequently Asked Questions
What is the sequence length of Wan 3.0’s attention mechanism? The T2V-1.3B uses standard full attention with a sequence length determined by the latent frame count. Typical configurations use 40–80 latent frames.
Does Wan 3.0 support CFG (Classifier-Free Guidance)? Yes — CFG is supported with guidance scales from 1.0 (no conditioning) to 15.0 (strong conditioning). Recommended range is 5.0–9.0.
Can I export Wan 3.0 to ONNX or TensorRT? The model is distributed in PyTorch format. Community efforts are underway for TensorRT optimization. The T2V-1.3B can be exported to ONNX with TorchScript.
What video codec does Wan 3.0 output use? Output is generated as frames and encoded to H.264 MP4 by default. Alternative codecs (HEVC, AV1) can be configured in post-processing.
How does the 3D causal VAE differ from standard VAEs? The 3D causal VAE processes spatial and temporal dimensions jointly, enabling consistent latent representations across frames — critical for temporal coherence in generated video.
Key Takeaways
1. Wan 3.0 (https://www.wan-3.co) uses a diffusion transformer with flow matching — state-of-the-art architecture for video generation
2. Four model variants cover consumer GPU (1.3B) to production (14B) workloads
3. 3D causal VAE enables 1080p encoding despite native 480P–720P output
4. Standard Diffusers API for easy integration with existing ML pipelines
5. For engineers needing turnkey 1080p video APIs, Kling 3.5 (https://www.kling35.org) at https://www.kling35.org offers a simpler alternative
References
1. Wan 3.0 Official Site (https://www.wan-3.co)
2. Kling 3.5 AI Video Generator (https://www.kling35.org)
3. Runway Gen-4 (https://runwayml.com)
4. Sora — OpenAI (https://openai.com/sora)
5. Apache 2.0 License (https://www.apache.org/licenses/LICENSE-2.0)
