InVideo vs Local AI Video: One Costs $0 Plus Your Weekend — the Other Costs $17

The proprietary video market has been chaotic. OpenAI shut down the Sora consumer app in March 2026, less than six months after launch, after downloads fell roughly 66% from their peak (the API remains live separately). ByteDance's Seedance 2.0 ran into Hollywood lawsuits and a paused global rollout the same month, after cease-and-desist letters from Disney, Paramount, and Warner Bros. — it remains accessible in China but carries legal risk for international commercial use. Alibaba's HappyHorse model topped the quality leaderboards in April 2026 — and never opened to the public.

That chaos is exactly what makes both doors attractive. Open local models make you independent of vendor drama. And InVideo absorbs the drama for you: its subscription bundles access to 200+ models — including Kling 3, Veo 3.1, and Seedance 2.5 — so when one model disappears or gets sued, your workflow doesn't notice.

Three open-weights systems dominate local video generation right now, measured by downloads, community activity, and benchmark results. All three run through ComfyUI, a node-based interface installed on your own machine — not a chat-style tool like Ollama. These are diffusion models, not LLMs.

Wan 2.2 is the most widely deployed open video model: its I2V-A14B repository alone recorded roughly 4.24 million Hugging Face downloads in a single month, with hundreds of community derivatives built on top. It ships in three variants — T2V-A14B and I2V-A14B (mixture-of-experts, 27B total / 14B active parameters), plus a compact TI2V-5B that handles both text- and image-to-video on as little as 6–8GB VRAM. The 14B tier needs 15GB (GGUF Q3) to 25GB (FP8); the official unquantized command asks for 80GB. Its license is Apache 2.0 — genuinely free, unrestricted commercial use, no revenue thresholds, no territory exclusions.

Speed, concretely: a single 5-second clip takes roughly 4–9 minutes on an RTX 4090 (one independently reported figure — Wan 2.2 doesn't natively output longer clips in one pass). To build a 20-second sequence, you'd generate 4 separate 5-second clips and stitch them — call it 16–36 minutes of raw generation time, plus manual editing to join them smoothly. That range is an extrapolation from the per-clip figure, not a directly measured 20-second benchmark.

LTX-2 is the only open model in this trio that generates synchronized audio and video in a single pass — footsteps, ambience, and effects arrive with the picture. It is also the fastest of the three and the most forgiving on hardware. The architecture is a 22B diffusion transformer; LTX-2.3 (March 2026) remains fully supported alongside the current LTX-2.5 release. The license is the LTX Community License — free for commercial use if your company's total revenue is under $10M per year, with a paid commercial license required above that threshold. (Some third-party write-ups incorrectly call it Apache 2.0 — the official license page is the only reliable source.) Hardware needs run 18–20GB VRAM quantized, 32GB+ at full precision; on 12GB cards, the older LTX-Video 0.9.5 remains the practical choice.

Speed, concretely: LTX-2 is qualitatively the fastest of the trio, with near-real-time previews on high-end consumer cards — but no independently verified minutes-per-clip figure on an RTX 4090 exists as of this writing, so we won't invent one. The one hard number available is from Lightricks' own benchmark on datacenter-class "Nvidia superchips" (not a consumer GPU): a 10-second clip in about 6.8 seconds. Treat that as a ceiling for what the architecture can do on serious hardware, not what your home rig will see.

Tencent's 8.3B model, released November 2025, is a community favorite for cinematic lighting and texture, and the lightest of the trio on VRAM: 14GB minimum with offloading, 24GB comfortable, at roughly 75 seconds per 480p clip on an RTX 4090. It generates 480p/720p natively, up to 1080p via built-in super-resolution, clips up to 10 seconds.

Speed, concretely: at ~75 seconds per 5-second 480p clip, that's roughly 15 seconds of render time per second of video. Its native max clip length is 10 seconds, so a 20-second sequence means two generations at max length — extrapolating the per-second rate, call it roughly 5 minutes of raw generation for 20 seconds of footage, before stitching. This is an extrapolation from the sourced 5-second figure, not a directly measured 10-second or 20-second benchmark.

Released August 3, 2026, MiniMax H3 is a 33.1B omni-modal model with native stereo audio, day-one ComfyUI support, and quantized versions that run on an RTX 3060. Two caveats before treating it as a fourth pick: the local release caps at 768p (the full 2K pipeline stays hosted-only), and its Community License reportedly carries its own geographic restrictions and a $20M revenue threshold — check the official model card before committing. Early signs are strong, but three weeks old and production-ready are different things.

Local video generation is free the way a puppy is free: the model weights cost nothing, but the GPU is the real price of entry. Skip local generation entirely if your GPU has under 12GB VRAM and you're not planning to upgrade — none of the three models above run at usable quality below that tier, and a cloud platform will get you better output faster.

Not sure what any of this means for your machine? These guides break it down: VRAM Calculator for exact requirements per model, How Much VRAM Do You Need? for charts across model sizes, Best GPUs for Local AI and Best Budget GPUs for hardware picks, and GPU vs CPU vs Apple Silicon for platform comparisons. One honest caveat: those guides use the LLM VRAM formula (parameters × bits ÷ 8). Video diffusion models also scale VRAM with resolution and clip length, so treat their numbers as a floor, not a ceiling, for video workloads.

With local models, you are not installing a video tool — you are assembling a pipeline.

The generation setup. ComfyUI is node-based: you build, or import and debug, a workflow graph of loaders, samplers, and decoders. Expect CUDA version mismatches, PyTorch pins, and the occasional flash_attn install error before your first frame renders.

The prompting. Video models need structured prompts — shot type, camera movement, lighting, subject action — not one-liners. There is no built-in prompt helper and no system-prompt layer; you write the full structure yourself. Our guides on system prompts vs. user prompts and prompt engineering for local models cover fundamentals that transfer directly to video prompting.

Everything around the clip. Local models output raw, silent (LTX excepted) clips of 5–20 seconds. Script, voiceover, music, stock footage, subtitles, and editing are each separate tools you choose, install, and wire together yourself.

The short version, mapped to common situations:

InVideo is probably the better choice if you:

• Don't own a powerful GPU
• Want to start creating videos immediately
• Don't want to install and configure ComfyUI, CUDA, models, or Python environments
• Want an integrated workflow rather than assembling multiple local tools
• Need scripts, voice, music, subtitles, and video generation in one workflow
• Care more about finished videos than experimenting with the underlying models

Local AI is probably the better choice if you:

• Already own suitable GPU hardware
• Want maximum control
• Want to experiment with models and workflows
• Have strong technical skills
• Prioritize keeping generation locally controlled
• Expect to generate very large volumes and want to optimize marginal generation cost

You don't need to commit to a local GPU setup — or a paid subscription — just to evaluate the cloud workflow. Before buying hardware or spending a weekend on ComfyUI, it's worth spending five minutes the other way first:

1. Try InVideo's free version. 2. Create one short video. 3. Evaluate the output quality and how the workflow felt. 4. Compare that experience against the setup effort a local install would take.

That turns the comparison from something you read about into something you can test yourself in less time than it takes to read the rest of this article.

Go local if you have (or will buy) a 12GB+ GPU, enjoy building your own tools, and value privacy and unlimited $0 generations over convenience. Wan 2.2 is the safest foundation — top quality, Apache 2.0, no fine print — with LTX-2 as the speed-and-sound specialist.

Go cloud if you don't have the hardware, don't want the setup, or need finished videos rather than raw clips. For most people who simply want to make AI-generated videos, the cloud route is the easier starting point: if you don't already have the hardware and technical interest local generation requires, InVideo removes most of that complexity in one prompt, with every model and asset bundled and automation included — starting at $0 to test and $17/month (billed annually) to remove the watermark. The simplest way to find out whether it fits your workflow is to try the free version.

Both doors lead to AI video. The question was never which technology is better — it's which workflow fits your machine, your patience, and your goals.