Best Local AI Video: Wan vs LTX-2.3 & More (2026)

📚 More on this topic: ComfyUI vs Automatic1111 vs Fooocus · VRAM Requirements Guide · What Can You Run on 24GB VRAM · Stable Diffusion Locally · Planning Tool

A year ago, local AI video generation was a novelty — 2-second clips at 480p with visible artifacts, taking 30 minutes to render. You’d show someone and say “isn’t that cool?” and they’d politely agree while looking at a melting face.

That’s not where we are anymore. Wan 2.2 generates coherent 5-second clips with smooth human motion. LTX-Video produces clips faster than real-time. HunyuanVideo 1.5 handles faces better than most cloud services. And all of it runs on hardware you can buy for under $2,000.

It’s still early. It’s still slow (mostly). The quality gap with top-tier cloud services is real. But if you have a GPU with 12GB+ VRAM and some patience, you can generate video locally that would have cost hundreds of dollars in cloud credits a year ago.

Here’s what actually works, what you actually need, and what’s still painful.

The Models: What’s Available Right Now

A handful of models matter for local video generation in 2026. They vary wildly in quality, speed, VRAM needs, and what they’re good at.

Wan 2.2 (Alibaba) — Best Overall

The open-source quality leader, and the family most people should start with. Apache 2.0 licensed, meaning full commercial use.

Two entry points worth knowing. The TI2V-5B dense model is the local sweet spot — 720p at 24fps on a single 4090 or an 8GB-class card, no quantization gymnastics required. The 14B A14B MoE is where Wan gets serious on quality. People who quote “24GB for Wan 14B” are describing a heavily-offloaded config; full FP16 with the T5-XXL text encoder resident actually wants datacenter memory (50GB+). The practical path is GGUF + T5 CPU offload, which the community has pushed down to roughly 6-8GB at 480p — a 14-billion-parameter video model on a $200-class GPU. Eighteen months ago this would have been absurd.

A word on the newer Wan versions. Alibaba has kept shipping — Wan 2.5, 2.6, and 2.7 all exist — but none is a clean open release the way 2.2 is. As of July 2026, the official Wan-AI Hugging Face org still tops out at Wan 2.2 (alongside 2.1) for downloadable, Apache 2.0-licensed weights — that’s the line readers actually run in ComfyUI. Wan 2.5 and 2.6 are closed, API/hosted-only. Wan 2.7 is a murkier case: it’s not on Hugging Face and not Apache-2.0-open, but some 2.7 weights have surfaced on Alibaba’s ModelScope (primary) and GitHub (secondary) under different, unclear license terms — so it’s obtainable if you go looking, but it isn’t the clean, permissively-licensed local release 2.2 is. Its hosted model adds 1080p and longer (~15-second) clips with native audio. For a local guide, Wan 2.2 is still the version that matters: it’s the open, licensed, downloadable ceiling, the most battle-tested locally, and the one with mature ComfyUI workflows.

Best for: Everything. If you’re picking one model, pick a Wan.

LTX-2.3 (Lightricks) — Fastest, and Now With Audio

The speed champion, and as of 2026 the first open model to generate synchronized audio in the same pass as the video.

LTX-2 was open-sourced in January 2026; LTX-2.3 (March 2026) is the current release — a 22B DiT model under Lightricks’ community license (free under $10M revenue), doing native 4K at 50fps with a 5B audio stream running alongside the 14B video stream. The distilled 8-step variant is the fast path; the dev variant is BF16 for maximum quality.

Read the speed line again. Near-real-time generation on a single consumer GPU. That changes the workflow completely: with every other model you type a prompt, wait 5-15 minutes, and hope. With LTX you iterate — try a prompt, see the result in seconds, adjust, try again, closer to how you’d use Stable Diffusion for images. And thanks to Q4 GGUF quantization, people have run 5-second clips with audio on a 10GB RTX 3080.

The quality trade-off is real — LTX doesn’t match Wan 14B or HunyuanVideo for fine detail per frame. But native 4K plus synchronized audio makes it the most complete single-model workflow in open source right now.

Best for: Rapid iteration, concept prototyping, and anything that needs audio without a second tool.

HunyuanVideo 1.5 (Tencent) — Best Faces

The cinematic quality leader, especially for anything involving human faces.

HunyuanVideo 1.0 was impressive but impractical — 40GB+ VRAM meant consumer GPUs were out. Version 1.5 was a breakthrough: they cut parameters from 13B to 8.3B, added sparse attention for 1.87x speedup, and brought VRAM down to 14GB with offloading.

The quality, especially on faces, is exceptional. Multiple characters in a scene, natural expressions, minimal artifacts. If your video involves people, HunyuanVideo is the model to try first.

The step-distilled variant generates in about 75 seconds on an RTX 4090 — not LTX-Video fast, but far from the 10-minute waits of earlier models.

Best for: Cinematic content, anything with human faces, multi-character scenes.

CogVideoX (Tsinghua/ZhipuAI) — Best Image-to-Video

CogVideoX has the best image-to-video mode among open models. Generate a hero image with FLUX.2 or SDXL, then animate it with CogVideoX. The 3D Causal VAE technology delivers strong detail preservation.

The downsides: 8 FPS output looks noticeably choppy compared to 24 FPS competitors, and the 15-minute generation time for the 5B model tests your patience. CogVideoX 1.5 pushes resolution to 1360x768 but needs even more VRAM.

Excellent ComfyUI support through the kijai/ComfyUI-CogVideoXWrapper node, which handles T2V, I2V, LoRA, and GGUF quantization.

Best for: Image-to-video animation, scientific/technical content.

AnimateDiff — Most Flexible (but Aging)

AnimateDiff doesn’t generate video from scratch — it adds a temporal motion module on top of existing Stable Diffusion checkpoints. This means you can use any of the thousands of community SD 1.5 or SDXL models and LoRAs, and AnimateDiff adds motion.

That flexibility is its superpower and its limitation. You get access to the massive SD ecosystem of styles, but the output quality is fundamentally limited by the base SD architecture. Motion can look jittery or looping rather than cinematic. The technique is showing its age against purpose-built video diffusion models.

Still worth knowing about if you’re already deep in the SD ecosystem and want to animate your existing workflows without learning a new model.

Best for: Stylized animations, leveraging existing SD checkpoints and LoRAs.

Mochi 1 (Genmo) — The Pioneer

Mochi 1 was the first truly capable open-source text-to-video model (Apache 2.0). It broke new ground in late 2024 and early 2025. Strong prompt adherence, good motion quality, and the first model that made people take local video generation seriously.

It’s been surpassed. Wan 2.2 produces better quality at lower VRAM. HunyuanVideo handles faces better. LTX-Video is orders of magnitude faster. Mochi’s 480p resolution and 20GB+ VRAM requirement make it hard to recommend over the newer options.

Best for: Historical interest. Use Wan 2.2 instead.

Stable Video Diffusion (SVD / SVD-XT) — Legacy

SVD was Stability AI’s video model. Image-to-video only — no text-to-video. 14 or 25 frames of subtle camera motion and scene animation. Stability removed it from their API in August 2025 and shifted focus elsewhere. The weights are still on HuggingFace if you want to try it, but the community has moved on.

Best for: Simple image animation if you’re already on a very tight VRAM budget.

What You Can Actually Run: The VRAM Reality Check

This is the section most guides skip. Here’s the honest truth about what works at each VRAM tier.

8GB VRAM (RTX 4060, RTX 3060 8GB)

The reality: You can generate video on 8GB — and the floor has actually dropped below that. Heavy GGUF quantization with T5 CPU offload now runs Wan 480p and HunyuanVideo 1.5 in the 6-8GB range, so even some 8GB-and-under cards are in play. The results at this tier are real but modest — 480p with limited detail. 12GB remains the comfortable baseline where things stop feeling like a fight; below that, expect to lean on quantization and patience.

12GB VRAM (RTX 3060 12GB, RTX 4070)

The reality: A major step up from 8GB. The GGUF-quantized Wan 14B is the standout — a 14-billion-parameter video model producing real quality on a $180 used GPU. This is the tier where local video generation starts being genuinely useful rather than just a tech demo. The RTX 3060 12GB remains the best budget entry point.

16GB VRAM (RTX 4060 Ti 16GB, RTX 4080)

The reality: This is where things get genuinely good. HunyuanVideo 1.5 becomes accessible — 720p with the best face rendering in open source. LTX-2 runs at full quality. Higher-quality GGUF quants of Wan 14B are available. If you’re buying hardware specifically for video generation, 16GB is the minimum to aim for.

24GB VRAM (RTX 3090, RTX 4090)

The reality: The sweet spot. Every model runs without painful compromises. You get full-precision weights, higher resolutions, and reasonable generation times. The RTX 4090 is about 40-70% faster than the RTX 3090 despite the same 24GB — faster CUDA cores and better memory bandwidth. The used RTX 3090 is still the bang-for-buck winner, though the 2026 memory-driven price surge has pushed it to roughly $1,000-1,300 rather than the ~$700 it sat at a year ago.

48GB+ (Multi-GPU, Mac Unified Memory)

At 48GB+ you run unquantized 14B models at full precision, generate longer clips, and push higher resolutions without offloading overhead. Mac M4 Max with 64-128GB unified memory handles everything but with slower compute than a dedicated NVIDIA GPU. This tier is for people who want zero compromises.

Honest Comparison: Local vs Cloud Services

Let’s not pretend local video generation matches the best cloud services. It doesn’t. But let’s also not pretend the gap is as wide as it was six months ago.

The Cloud Landscape

Note: OpenAI’s Sora 2 is being wound down — the consumer web/app shut down in April 2026 and the API follows in September 2026 — so it’s no longer a target worth benchmarking against.

Where Cloud Wins

• Peak quality. Runway Gen-4.5, Veo 3.1, and Kling 3.0 produce more consistently photorealistic results than any local model. The gap is narrowing but real.
• Speed of use. Cloud services return results in 10-60 seconds. Local models (except LTX) take 4-15 minutes.
• No hardware investment. No GPU purchase required — and with 2026 GPU prices where they are, that’s a bigger deal than it used to be.

Audio used to be a clean win for cloud — it isn’t anymore. LTX-2.3 generates synchronized dialogue, ambience, and lip-sync locally in a single pass. That was the single biggest thing local couldn’t do a few months ago, and now it can.

Where Local Wins

• Cost at volume. After hardware, generation is free. A creator making 50+ clips per day would spend hundreds per month on Runway. Locally: electricity.
• Privacy. Nothing leaves your machine. No content policy filters. No terms of service.
• No limits. No monthly credit caps. No watermarks. No content restrictions.
• Customization. LoRA training, model merging, ComfyUI workflow pipelines — you control every parameter.

The Honest Verdict

Wan 2.2 14B and HunyuanVideo 1.5 are competitive with mid-tier cloud offerings. For social media clips, B-roll footage, concept prototyping, and creative experiments, local is already “good enough.” For professional commercial content or anything requiring photorealistic human faces with complex interactions, cloud services still lead — but the margin is shrinking fast.

ComfyUI: The Video Generation Hub

Every serious local video model runs through ComfyUI. It’s become the universal interface for video generation, with official or community-maintained nodes for every model.

Which Models Have ComfyUI Support

Useful Workflows

The rapid iteration pipeline (LTX-2): Generate at 768x512 in seconds, iterate on prompts until you have what you want, then upscale winners with the built-in 4K spatial upscaler. The fastest path from idea to watchable video.

The quality pipeline (Wan 2.2 + upscaling): Generate at 480p/720p with Wan 2.2, upscale with RealESRGAN 4x, interpolate frames with RIFE or GIMM-VFI. More steps, better results.

The image-to-video pipeline (FLUX.2 + CogVideoX): Generate a hero image with FLUX.2 (the Nov 2025 successor to FLUX, with the sub-second FLUX.2 Klein variant for consumer GPUs) or SDXL, then animate it with CogVideoX’s I2V mode. Great for bringing still images to life with controlled motion.

Beyond ComfyUI

• Wan2GP (deepbeepmeep): Standalone web UI supporting Wan 2.1/2.2, HunyuanVideo, and LTX-Video. Five memory profiles for different hardware tiers. Simpler than ComfyUI if you just want to generate video without building node graphs.
• Pinokio: One-click installer for ComfyUI, Wan2GP, and other tools. Good for beginners who don’t want to deal with git clones and Python environments.

Speed Expectations: Don’t Sugarcoat It

This is where most guides get dishonest. Here are real generation times for a ~5-second clip.

RTX 4090 (24GB)

RTX 3090 (24GB)

Add 40-50% to all RTX 4090 times. A Wan 14B clip that takes 6 minutes on a 4090 takes about 9 minutes on a 3090.

RTX 3060 12GB

GGUF-quantized models only. Wan 14B Q5 at 480p: roughly 10-15 minutes per clip. LTX-Video FP8: still seconds.

The Reality

LTX-Video is the outlier — genuinely fast enough for iterative workflows. Everything else requires patience. You’re not going to sit there generating clip after clip like you would with cloud services. The workflow is more like: carefully craft your prompt, hit generate, go make coffee, come back and evaluate.

For comparison, cloud services return results in 10-60 seconds. The only local model that approaches cloud speed is LTX-Video.

This is a genuine limitation, not something you can optimize away. Video generation is computationally heavier than image generation by orders of magnitude. A 5-second video at 24 FPS is 120 frames — each requiring denoising passes through a multi-billion parameter model. The physics are what they are.

Best Setup for the Money

A note on prices: the 2026 memory shortage has inflated GPU pricing across the board, so these figures are higher than they were a year ago and move week to week. Treat them as ballpark.

Budget: Under $350

• GPU: Used RTX 3060 12GB (~$220-280)
• Models: Wan TI2V-5B, Wan 14B GGUF Q4-Q5, LTX-2.3 Q4
• What you get: Functional 480p-720p video generation, fast iteration with LTX
• Honest assessment: You can generate video. Results are usable for social media and experiments. Fine detail is limited.

Sweet Spot: ~$1,000-1,300

• GPU: Used RTX 3090 24GB
• Models: Everything runs — Wan 14B, HunyuanVideo 1.5, LTX-2.3, CogVideoX 5B
• What you get: Full-quality 720p from every model, reasonable generation times
• Honest assessment: Still the setup to buy. Every current model runs without painful compromises. The 3090 is old now, but 24GB of VRAM is 24GB of VRAM, and it’s the cheapest way into the 24GB tier.

Maximum Performance: $2,200+

• GPU: RTX 4090 (~$2,200-2,750 used/new) or RTX 5090 (32GB, ~$2,900-3,400 street)
• Models: Everything, 40-70% faster than RTX 3090; the 5090’s 32GB and NVFP4 support make full-precision 14B and 4K modes far less of a squeeze
• What you get: Near real-time with LTX-2.3, 75-second HunyuanVideo distilled clips, faster iteration across the board
• Honest assessment: The speed bump over the 3090 is significant if you generate a lot of video. If you’re experimenting occasionally, the 3090 is the better value — the current-gen premium is steep thanks to the price surge.

Where This Is Heading

The pace of improvement has been extraordinary:

• Early 2025: Best local option was CogVideoX at 720x480/8fps. Experimental at best.
• February 2025: Wan 2.1 released, immediately set a new quality standard.
• Mid 2025: GGUF quantization brought 14B models to 12GB cards.
• Late 2025: HunyuanVideo 1.5 cut VRAM by 40% while improving quality. LTX-Video proved real-time generation was possible.
• January 2026: LTX-2 open-sourced; NVIDIA shipped NVFP4/NVFP8 optimizations for RTX 40/50 cards.
• March 2026: LTX-2.3 landed native 4K/50fps with synchronized audio — the feature local generation had been missing.
• Spring 2026: Wan 2.5 (API-only) and Wan 2.7 (ModelScope-only, not a clean Apache-2.0 release) launched — so the openly-licensed Wan 2.2 stayed the local leader.

In about 18 months, local video generation went from “technically possible but painful” to “genuinely useful on mainstream hardware,” and the two things it couldn’t do a year ago — audio and multi-second length — both landed.

What’s Coming

• Longer videos. The 5-10 second cap is lifting: LTX-2.3 already reaches ~20 seconds locally, and extension/stitching workflows push further.
• Cheaper VRAM floors. Q4 GGUF + offload has already dropped the entry point to 6-8GB; expect the “you need 12GB” advice to keep eroding.
• Better distillation. HunyuanVideo’s 75-second generation time showed what distilled models can do; every major model now ships or is getting a fast variant (LTX’s 8-step distill is the template).
• The wildcard: GPU prices. The biggest limiter in 2026 isn’t the models — it’s what a 24GB card costs. If the memory shortage eases, this whole space gets more accessible overnight.

When Will Local Be “Good Enough”?

It depends on what you’re making:

The most likely future: local models handle iteration, drafting, and high-volume generation. Cloud services handle final renders for premium content. Hybrid workflows, just like how professionals use local Stable Diffusion for concept art and Midjourney for client-ready finals.

The Bottom Line

Local AI video generation is real, useful, and improving faster than any other area of consumer AI. A used RTX 3090 and ComfyUI gives you access to every major model. Wan for quality, LTX-2.3 for speed and audio, HunyuanVideo for faces.

Is it as good as Runway Gen-4.5 or Veo 3.1? No. Not yet. But it’s free after hardware, private, unlimited, uncensored — and it now does synchronized audio, which was the last thing you genuinely had to leave the machine for. The gap is closing fast enough that what’s “not quite there” today will likely be competitive within the year.

If you’ve been waiting for local video generation to be worth trying — it is now.