Local Video Models Compared: What Actually Runs on Consumer Hardware

TL;DR

• Wan2.1 T2V-1.3B is the most accessible for consumer GPUs at 8.19GB VRAM
• CogVideoX-5B runs as low as 5GB with optimizations
• Mochi 1 offers 480p quality but needs 60GB native or 20GB optimized
• LTX-Video needs 32GB+ GPU memory for local inference

Not all local video models are created equal when it comes to hardware requirements. While many models require cloud APIs, several open-source options can run locally—if your GPU has enough VRAM. Here's a breakdown of what actually works on consumer hardware.

The Consumer GPU Reality

The fundamental question for local video generation is simple: how much VRAM does it need? The answer varies dramatically across models. Some require 8GB, others need 60GB or more. Understanding these requirements is critical before downloading multi-gigabyte model weights (source).

Mochi 1: The 10B Parameter Challenge

Mochi 1 is a 10-billion-parameter video generation model from Genmo. The model card states it requires approximately 60GB VRAM when running on a single GPU natively (source).

This reflects Mochi's architecture: a 10B-parameter Asymmetric Diffusion Transformer (AsymmDiT) paired with a T5-XXL text encoder and a custom VAE. The visual stream alone has nearly 4 times the parameters of the text stream, demanding substantial memory.

Can it run on consumer hardware? With ComfyUI optimizations—CPU offloading and FP8 quantization—the VRAM requirement drops to under 20GB. The ComfyUI-MochiWrapper requires ComfyUI with updated PyTorch dependencies — see ComfyUI installation troubleshooting for common issues.

Mochi generates at 480p resolution natively (source). The model was originally trained on 4×H100 GPUs (Civitai community report 8313), making the compression to consumer hardware a significant achievement.

LTX-Video: Real-Time Generation on H100

LTX-Video from Lightricks takes a different approach. The paper describes a transformer-based latent diffusion model with a Video-VAE achieving a 1:192 spatiotemporal compression ratio. By relocating patchification to the VAE input, the architecture enables full spatiotemporal self-attention in a highly compressed latent space—making real-time generation tractable on H100-class hardware.

The distilled 2B variant claims 15× faster inference than non-distilled, with no CFG or STG requirements. However, these claims are sourced from the LTX-Video repo release notes rather than an independent benchmark, and explicit consumer GPU (RTX 4090) benchmarks are not provided.

Consumer GPU viability: The 32GB+ ComfyUI minimum targets professional hardware rather than consumer GPUs. The LTX-Video paper benchmarks on H100, not RTX-class GPUs, and the distilled 2B variant's "15× faster" claim comes from the LTX-Video repo release notes—not an independent benchmark.

CogVideoX-5B: The Low-VRAM Option

CogVideoX-5B from THUDM offers a different proposition. The model card explicitly states that diffusers BF16 inference starts from 5GB VRAM with CPU offloading enabled.

This makes CogVideoX-5B one of the most accessible options for local video generation. The 5GB figure assumes optimizations like enable_model_cpu_offload() and vae.enable_tiling() are active. Without these, peak VRAM jumps to roughly 15GB.

Key specifications (source):

• Resolution: Fixed at 720×480 (16:9 aspect ratio)
• Duration: 6 seconds (source)
• Frame rate: 8 FPS (source)
• Prompt length limit: 226 tokens (English only) (source)

The model supports INT8 quantization via PyTorchAO, theoretically enabling runs on free T4 Colab instances or smaller consumer GPUs. However, this requires PyTorch nightly and source builds—non-trivial setup for most users.

Wan2.1 T2V-1.3B: The Clear Consumer Winner

Wan2.1 T2V-1.3B from the Wan Team makes a bold claim: 8.19 GB VRAM is sufficient for inference on consumer-grade GPUs. The model card explicitly states it can generate a 5-second 480P video on an RTX 4090 in about 4 minutes without additional optimization.

This makes Wan2.1 T2V-1.3B the only model in this comparison with an explicit claim of running on "almost all consumer-grade GPUs." The architecture achieves this through careful design: a 1.3B-parameter DiT with a T5-XXL text encoder, but with fewer layers and smaller feedforward dimensions than the 14B variant (source).

The model supports both 480P and 720P resolution, though 480P is recommended for stability. The inference code provides --offload_model True and --t5_cpu options to further reduce VRAM requirements on marginal hardware (source).

HunyuanVideo: The High-End Option

For more on VRAM requirements for image and video models, see VRAM requirements guide.

This 13B+ parameter model uses a 3D causal VAE and MLLM text encoder. While the FP8 quantized weights reduce memory somewhat, the baseline requirement exceeds most consumer GPUs. The HunyuanVideo card also notes 45GB VRAM is needed for the smaller 544×960 resolution variant.

Kling AI: Cloud-Only Access

Kling AI from Kuaishou represents the API-only approach. The service launched globally with 66 daily "Inspiration Credits" (source, roughly equivalent to six free videos). Subscription tiers in China range from RMB66 to RMB666 monthly (source).

The platform supports text-to-video, image-to-video, and various editing capabilities. However, no local inference option exists—the model is exclusively web-accessible.

Model Comparison Table

Key Considerations

Resolution and Frame Rate

Most open-source models generate at lower resolutions than commercial APIs. CogVideoX-5B and Wan2.1 T2V-1.3B both produce 480P video. Mochi and LTX-Video support higher resolutions but require more VRAM (Mochi source).

Frame rates vary: CogVideoX uses 8 FPS, while LTX-Video targets 30 FPS for real-time output.

Prompt Requirements

English-language prompts work best across all models. CogVideoX-5B and Mochi both have 226-token prompt limits. Wan2.1 supports Chinese and English text generation, making it unique among the local options.

Quality vs. Speed Trade-offs

For practical workflows to speed up inference across these models, see Video generation speedup guide.

Community Optimizations

Many "local" models rely on community wrappers for practical consumer use. The ComfyUI-MochiWrapper enables Mochi on consumer hardware through CPU offloading and FP8 quantization. Similar community tools exist for CogVideoX and LTX-Video, though they're not officially documented.

Recommendations

For RTX 4090 owners: Wan2.1 T2V-1.3B offers the best balance of quality and accessibility. CogVideoX-5B provides a simpler setup with lower VRAM requirements.

For lower VRAM GPUs (12GB or less): CogVideoX-5B with diffusers CPU offloading is your best bet. The INT8 quantization path theoretically enables runs on even smaller GPUs.

For maximum quality (and 40GB+ VRAM): Mochi 1 and LTX-Video deliver superior output, though LTX-Video's real-time capability on H100-class hardware is unmatched.

For cloud GPU workflows and runpod-style setups: see Cloud GPU training guide for the Mochi latent-decode split workflow.

For API access: Kling AI and LTX-Studio provide immediate access without hardware investment, though at the cost of control and ongoing usage fees.

Common Errors and Fixes

Mochi requires careful GPU configuration: FP8 quantization must be paired with CPU offloading to meet 20GB target. Ensure ComfyUI-MochiWrapper is properly installed with current PyTorch dependencies.

LTX-Video needs substantial VRAM: Even the distilled 2B variant requires 32GB+ on ComfyUI. Consider cloud inference (LTX-Studio) or upgrade GPU beyond 32GB capacity.

CogVideoX optimization is complex: Requires PyTorchAO for INT8 quantization and proper CPU offloading. Use diffusers with CPU offloading, avoid PyTorchAO unless experienced.

Wan2.1 has specific hardware requirements: 8.19GB minimum, but optional optimizations can increase VRAM usage. Use --offload_model True and --t5_cpu for lower VRAM usage.

HunyuanVideo is a professional-class model: Requires 60GB+ VRAM, exceeding most consumer GPUs. Use cloud services or consider the 14B variant with 60GB+ VRAM.

FAQ

Can I run Mochi 1 on a single consumer GPU? With ComfyUI optimizations and FP8 quantization, yes—on RTX 3090/4090 class hardware. Native VRAM requirement is ~60GB.

What is the minimum VRAM for LTX-Video local inference? The ComfyUI integration specifies 32GB+ for LTX-2. Original LTX-Video VRAM requirements for consumer GPUs are not explicitly documented.

How does CogVideoX compare to Wan2.1 for consumer hardware? CogVideoX-5B runs on as little as 5GB with optimizations. Wan2.1 T2V-1.3B requires 8.19GB but generates faster (4 minutes vs 3+ minutes for similar quality).

Which local video model runs best on an RTX 4090? Wan2.1 T2V-1.3B offers the best price/performance for 4090 owners. For maximum quality, consider Mochi 1 with ComfyUI optimizations.

Is Kling AI available as an API or only web? Kling AI is currently web/API-only. No local inference option exists (source).

Sources