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Best AI Video Generation Models for Local Hardware in 2025

Compare the best open-source AI video generation models for local use: Wan Video 2.1, LTX Video, HunyuanVideo, CogVideoX, Mochi 1, and AnimateDiff. Accurate VRAM requirements by configuration, quality, and setup for each.

AI video generation has progressed rapidly, and several open-source models now produce genuinely impressive results on local hardware. The VRAM requirements vary dramatically depending on resolution, precision, and offloading strategy — so we present ranges rather than single numbers. This guide covers every major open video model, what hardware you actually need, and which ones are worth your time.

Video Models at a Glance

ModelParamsVRAM RangeMax ResolutionMax FramesQualityLicense
Wan Video 2.1 14B14B8-55 GB720p81ExcellentApache 2.0
Wan Video 2.1 1.3B1.3B8 GB480p81GoodApache 2.0
LTX Video 2B2B6-12 GB720p161GoodResearch
LTX Video 13B13B16-45 GB720p257Very goodResearch
HunyuanVideo13B31-60 GB1280x720129Very goodCommunity
CogVideoX 5B5B12-18 GB720x48049GoodResearch
Mochi 1 Preview10B20-60 GB848x48084GoodApache 2.0
AnimateDiff v1.5.30.4B adapter6 GB512x51216ModerateApache 2.0

Tier 1: Best Quality (Datacenter / High-VRAM)

Wan Video 2.1 14B — State of the Art

Wan Video 2.1 14B from Alibaba is the current quality leader among open video models. Its 14B parameter 3D DiT architecture produces videos with exceptional motion quality, temporal coherence, and visual detail. Critically, the VRAM requirements depend heavily on your workflow.

VRAM by configuration:

  • 480p, official path with offload: ~28 GB
  • 720p, official path with offload: ~55 GB
  • 480p, community low-VRAM (t5_cpu): as low as ~8.2 GB
  • Full precision, no offload: 40-55 GB depending on resolution

The t5_cpu technique offloads the 9.4B T5 text encoder to system RAM, dramatically cutting VRAM usage. Community workflows in ComfyUI and diffusers have made this the standard approach for consumer GPUs.

Output: Up to 720p at 16fps, 81 frames (~5 seconds). Supports both text-to-video and image-to-video.

Strengths: Best motion quality of any open model. Natural camera movement and object physics. Apache 2.0 license for commercial use. Community offloading workflows bring it within reach of 12GB GPUs at 480p. Growing LoRA ecosystem for character and style customization.

Weaknesses: 720p at full quality still requires 55GB. Low-VRAM community workflows trade speed for accessibility — generation is significantly slower with offloading. Quality at 480p is below what 720p delivers.

Who should use it: Anyone serious about video quality. The community low-VRAM path makes it accessible even on consumer GPUs, though datacenter hardware (A100, H100) or a high-memory Mac delivers the best experience.

HunyuanVideo — High Fidelity From Tencent

HunyuanVideo is Tencent's 13B parameter video model. Competitive with Wan Video 14B on visual fidelity with strong temporal coherence.

VRAM by configuration:

  • 544x960, 129 frames (FP16): ~45 GB
  • 720x1280, 129 frames (FP16): ~60 GB
  • FP8 precision: reduces above figures by ~30% (31-42 GB)

Output: Up to 1280x720 at 24fps, 129 frames (~5 seconds). Text-to-video.

Strengths: Up to 24fps for smoother video. 129 frame support for longer sequences. FP8 quantization meaningfully reduces VRAM without severe quality loss. Strong temporal coherence across longer clips.

Weaknesses: Even with FP8, 31GB+ VRAM puts this beyond most consumer GPUs. Tencent Community license has restrictions. Smaller community than Wan Video.

Who should use it: Users on datacenter hardware or high-VRAM workstations (A100, dual 3090s) who want an alternative to Wan Video 14B, particularly when higher framerate or FP8 support matters.

Tier 2: Consumer-Accessible Quality

Wan Video 2.1 1.3B — Best Quality on Consumer Hardware

Wan Video 2.1 1.3B is the lightweight variant of Wan Video, distilled down to 1.3B parameters. It runs on consumer GPUs with 8GB+ VRAM while maintaining surprisingly good quality for its size.

VRAM: ~8 GB at FP16. Fits on an RTX 4060 or better.

Output: 480p at 16fps, up to 81 frames (~5 seconds). Text-to-video.

Strengths: Runs on 8GB VRAM — the most accessible quality video model. Same architecture as Wan 14B, distilled for efficiency. Apache 2.0 license. Good quality-to-VRAM ratio. Good entry point for learning video generation workflows.

Weaknesses: Quality notably below the 14B variant — softer details, less precise motion. 480p maximum resolution limits output usability. Generation still takes 2-5 minutes per clip on an RTX 4090.

Who should use it: Anyone with a consumer GPU (RTX 4060 or better) who wants to generate videos locally without offloading complexity. The best starting point for most users.

LTX Video — Fast and Flexible (2B + 13B)

LTX Video from Lightricks ships in two sizes: a 2B model and a 13B model, each available in dev, distilled, and FP8 variants. Frames follow the 8n+1 pattern (25, 33, 81, 161, 257).

VRAM by variant:

  • 2B distilled: 6-12 GB depending on resolution (720p comfortable on 12GB)
  • 2B FP8: reduces VRAM by ~50% vs FP16
  • 13B dev (FP16): 32-45 GB depending on resolution
  • 13B FP8: 16-24 GB

Output: Up to 720p at 24fps. 2B supports up to 161 frames (~7s), 13B up to 257 frames (~11s). Text-to-video and image-to-video.

Strengths: 2B distilled is among the fastest open video models — good for rapid prompt iteration. The 13B produces the highest quality in the LTX family. FP8 variants halve VRAM. Spatial and temporal upscalers available for post-processing.

Weaknesses: Maximum resolution is 720p (not 4K as sometimes claimed). 2B distilled quality is below dedicated 10B+ models. Research license limits commercial use.

Who should use it: The 2B distilled is ideal for rapid experimentation on consumer GPUs (12GB+). The 13B FP8 is accessible on RTX 4090 (24GB) for higher quality.

CogVideoX 5B — Research-Grade

CogVideoX 5B from Tsinghua University is a 5B parameter model focused on research applications. It uses a 3D full-attention transformer with expert adaptive LayerNorm.

VRAM by configuration:

  • BF16 (official format), 720x480x49f: ~18 GB
  • INT8 via diffusers: ~12 GB

Note: the official weights are BF16, not FP16. INT8 quantization is available through the diffusers library for lower VRAM usage.

Output: 720x480 at 8fps, 49 frames (~6 seconds). Text-to-video.

Strengths: INT8 path brings it to 12GB GPUs. Solid research baseline. 3D full-attention for temporal coherence. Academic origins with solid documentation.

Weaknesses: 8fps is low — videos appear choppy. 720x480 resolution is below modern expectations. Research license.

Who should use it: Researchers and developers exploring video generation architectures. The INT8 path makes it accessible on 12GB GPUs for experimentation.

Tier 3: Specialized Options

Mochi 1 Preview — Apache 2.0 Quality

Mochi 1 Preview from Genmo is a 10B parameter model notable for its permissive Apache 2.0 license and strong motion quality.

VRAM by configuration:

  • Full precision: ~60 GB (H100 recommended)
  • diffusers model offloading: ~22 GB
  • ComfyUI optimized workflows: under 20 GB

Output: 848x480 at 30fps, up to 84 frames (~3 seconds). Text-to-video.

Strengths: Apache 2.0 license — one of the most permissive. 30fps output for smooth video. Strong motion quality from the AsymmDiT architecture. ComfyUI community has pushed VRAM below 20GB.

Weaknesses: 848x480 resolution is limiting. Full precision requires datacenter VRAM. Offloaded mode is slow. No LoRA ecosystem. Short maximum clip length (3 seconds).

Who should use it: Developers building commercial video generation applications who need a permissive license. The ComfyUI optimized path makes it viable on an RTX 4090.

AnimateDiff v1.5.3 — Motion Adapter for SD 1.5

AnimateDiff is not a standalone video model — it is a motion adapter module (0.4B parameters) that plugs into any Stable Diffusion 1.5 checkpoint. It requires an SD 1.5 base model to function. This means you can generate animated versions of images using any SD 1.5 fine-tune and its full ecosystem.

VRAM: ~6 GB total (SD 1.5 base + 0.4B motion adapter). Runs on almost any modern GPU.

Output: 512x512 at 8fps, 16 frames (2 seconds). Works with any SD 1.5 checkpoint.

Strengths: Leverages the massive SD 1.5 ecosystem — use Realistic Vision for photorealistic animation, DreamShaper for stylized animation. Full compatibility with SD 1.5 ControlNets and LoRAs. Extremely low VRAM requirement. Apache 2.0 license.

Weaknesses: Requires an SD 1.5 base model — does not work standalone. 512x512 at 8fps is low by modern standards. Limited to 2-second clips. Motion quality is below dedicated video models.

Who should use it: Users who want to animate existing SD 1.5 workflows. Excellent for creating short animated loops, GIF-style content, or adding subtle motion to still compositions.

Hardware Recommendations by Budget

Budget ($250-400): RTX 4060/4070

At 8-12GB VRAM, your options are:

  • LTX Video 2B distilled — fast generation, best experience at this tier (the 13B FP8 variant also fits at 16GB for higher quality)
  • Wan Video 2.1 1.3B — best quality you can get at 8GB
  • Wan Video 14B (t5_cpu offload) — 480p on as little as 8.2GB, slow but high quality
  • AnimateDiff — animate any SD 1.5 model at 6GB

Mid-Range ($500-1000): RTX 4070 Ti Super / 4080

At 16GB VRAM:

  • LTX Video 2B distilled — runs comfortably with headroom
  • CogVideoX 5B INT8 — fits at 12GB with room to spare
  • Wan Video 14B (t5_cpu offload) — 480p generation is practical at this tier

High-End ($1600-2500): RTX 4090 / 5090

At 24-32GB VRAM:

  • Mochi 1 — ComfyUI optimized path under 20GB, diffusers at ~22GB
  • Wan Video 14B (official offload) — 480p at ~28GB is comfortable on a 4090
  • All Tier 2 models — run with fast generation and VRAM to spare

The RTX 4090 is the sweet spot for consumer video generation. The RTX 5090 at 32GB opens up Wan 14B at 480p without aggressive offloading.

Professional: A100 / H100 / Mac Ultra

At 48-192GB:

  • Wan Video 2.1 14B — 720p at full quality (~55GB)
  • HunyuanVideo — 1280x720 at FP16 (~60GB) or FP8 (~42GB)
  • LTX Video 13B — highest quality open video (720p, 32GB+)
  • Mochi 1 — full precision at 60GB

If video generation is a serious part of your workflow, datacenter hardware or a high-memory Mac makes the experience dramatically better.

The State of Local Video Generation

Local video generation has matured significantly. The two biggest shifts: LTX Video now spans a full family (2B for speed, 13B for quality) covering consumer through prosumer hardware, and community offloading workflows (especially t5_cpu) have brought Wan Video 14B within reach of 8-12GB GPUs. The gap between "official VRAM requirements" and "what the community has achieved" is often 3-5x.

The practical recommendations today:

Check which video models fit your hardware | Browse all models

Related reading: Best Local Image Generation Models | Best GPU for Running LLMs Locally | GGUF Quantization Explained

Frequently Asked Questions

Can I generate AI videos locally?

Yes. Several open-source video models run on consumer hardware. LTX Video ships as a 2B and 13B family — the 2B distilled is one of the fastest options on 12GB+ GPUs, while the 13B FP8 fits on an RTX 4090 for higher quality. Wan Video 2.1 1.3B runs on 8GB VRAM. AnimateDiff (a motion adapter for SD 1.5) needs only 6GB. Larger models like Wan Video 14B and HunyuanVideo require 28-60GB depending on resolution and offloading configuration.

What is the best open-source video generation model?

Wan Video 2.1 14B produces the highest quality videos among open models, with exceptional motion and visual fidelity. With community offloading workflows and t5_cpu, it can run on as little as 8.2GB VRAM at 480p. For consumer hardware without offloading, LTX Video offers two options: the 2B distilled for fast generation on 12GB+ GPUs, and the 13B (FP8) for higher quality on 24GB+ GPUs.

How much VRAM do I need for AI video generation?

It depends on the model and configuration. AnimateDiff needs only 6GB (SD 1.5 base + motion adapter). LTX Video 2B runs on 6-12GB. Wan Video 14B ranges from 8.2GB (480p with t5_cpu offload) to 55GB (720p official). HunyuanVideo needs 31-60GB depending on resolution and precision. VRAM requirements vary widely by offloading strategy.

Is AI video generation slow on consumer hardware?

It varies. LTX Video's 2B distilled variant is among the fastest, generating short clips in under a minute on modern GPUs. The 13B variant trades speed for quality but still runs well on high-VRAM cards. Most other models take 2-10 minutes per clip at 480p on an RTX 4090. Higher resolutions and longer clips take proportionally longer. Video generation remains the most VRAM-intensive diffusion task.

Can I run video AI models on Apple Silicon?

Yes, with limitations. Apple Silicon's unified memory makes it possible to load large models that wouldn't fit in discrete GPU VRAM. A Mac M4 Max with 64GB can run Wan Video 14B. However, generation is slower than on NVIDIA GPUs with equivalent memory bandwidth.