Local Text-to-Video on Low VRAM (2026): 6-8GB & CPU

On low-end hardware in 2026, the best local text-to-video pick is Wan 2.2 TI2V-5B at FP8 — it generates a 5-second 720p clip and fits an 8GB GPU once you turn on ComfyUI's offloading, while a GGUF-quantized Wan 2.2 14B (Q4/Q5) can squeeze onto a 6GB card at 480p by offloading the text encoder to system RAM. Both need 24GB or more of system RAM (32GB recommended) to hold what spills off the GPU. CPU-only generation technically works but is painfully slow — plan for many minutes to hours per short clip — so for low-end machines the realistic order is: 5B on an 8GB GPU first, 14B GGUF on 6GB second, and the cloud third when you need 720p/14B at usable speed.

This guide is deliberately separate from our full Wan video generation guide and HunyuanVideo guide, which cover the high-end paths. Here the entire focus is the 6-8GB and CPU reality: what actually fits, what resolution and length you can expect, and when you should stop fighting your hardware and rent a GPU instead.

What system RAM do you need for local text-to-video?

This is the question most low-VRAM guides skip, so let's answer it head-on: for local text-to-video you typically want at least 24GB of system RAM, with 32GB strongly recommended. Video models are not just about VRAM. When you run a quantized 14B model on a 6-8GB GPU, the trick that makes it fit is offloading the large T5 text encoder (and sometimes whole transformer blocks) into system RAM. If you only have 16GB of RAM, those offload paths run out of room and you either crash or fall back to disk swap, which is glacial.

A useful rule of thumb: your system RAM should be at least your model's full-precision size plus headroom. A 14B video model is roughly 28GB at FP16, so even quantized, the offload buffers plus the OS plus ComfyUI itself comfortably want 32GB. The 5B model is lighter, but 24GB is still the sane floor once you add the VAE decode step, which is memory-hungry on its own.

The takeaway: if you are building or upgrading for local video, adding system RAM is often cheaper and more impactful than chasing more VRAM — 32GB of DDR4/DDR5 costs far less than the GPU jump from 8GB to 16GB, and it is what unlocks the offloading that makes small GPUs viable.

Wan 2.2 5B vs 14B on low VRAM: which fits your card?

Wan 2.2 from Alibaba's Wan team ships in three official variants, and the size labels matter a lot for low-end hardware:

The headline numbers from the official Wan 2.2 repository look scary — the 5B "needs 24GB," the 14B "needs 80GB." Those are the full-precision figures for the unquantized checkpoints. What makes low-VRAM work is quantization plus offloading:

• The 5B (TI2V-5B) is the unified text/image-to-video model and the right starting point for an 8GB GPU. Kijai's FP8 build gets the weights down to roughly 5-6GB, and with ComfyUI's native offloading the community consensus is that the 5B adapts well to 8GB cards. The official repo cites a 5-second 720p clip in under 9 minutes on a single consumer GPU; on an 8GB card with offloading expect that to be slower.
• The 14B (T2V/I2V-A14B) is a Mixture-of-Experts design. You cannot run the raw checkpoint on a small GPU, but GGUF builds at Q3_K/Q4_K, combined with running the T5 text encoder on the CPU, let it run on cards as small as 6GB (RTX 2060 / RTX 4050-laptop class) at 480p. The trade is speed: on a 6GB card, community workflows commonly report 480p 14B clips taking 10-15+ minutes per run.

So the practical decision is simple. 8GB GPU? Start with the 5B FP8. It is faster and avoids the heavy offloading. Only 6GB, or you specifically want the 14B's higher fidelity? Use a 14B GGUF (Q4) at 480p and accept the slower runtime. For the full quality-focused walkthrough of both, see the Wan video generation guide.

How do I run Wan 2.2 14B GGUF on 6GB? (ComfyUI offload flags)

The reason a 14B model fits 6GB at all is GGUF quantization (smaller weights) plus aggressive offloading of everything that does not have to live on the GPU during the diffusion step. In ComfyUI, the relevant launch flags are documented in the project's CLI args:

# Low-VRAM (6-8GB): run text encoders on CPU, partial GPU loading
python main.py --lowvram

# When --lowvram still OOMs (very tight 4-6GB): more aggressive offload
python main.py --novram

# Reserve some VRAM for your desktop/other apps (helps avoid OOM)
python main.py --reserve-vram 1.0

# CPU-only fallback (works, but very slow)
python main.py --cpu

A few honest pointers that save a lot of failed runs:

1. Use a GGUF model loader node (the community ComfyUI-GGUF nodes) for the 14B, not the full safetensors loader — that is what makes Q4/Q5 weights load.
2. Keep the T5 text encoder on the CPU. It is large and you do not need it on the GPU during sampling; offloading it is what frees the VRAM for the actual video transformer.
3. Drop resolution and frame count first. On 6GB, 480p with fewer frames is the difference between "renders" and "out of memory." Push to 720p only once a 480p run succeeds.
4. Standalone apps like Wan2GP ("a fast AI video generator for the GPU poor") wrap these tricks for you and target as little as 6GB VRAM, supporting Wan 2.1/2.2, LTX-2 and Hunyuan Video — a good option if you do not want to wire up ComfyUI nodes by hand.

If you are new to ComfyUI itself, our complete ComfyUI guide covers installation, the node graph and where these model files go before you tackle a low-VRAM video workflow.

Is LTX-2 a good low-VRAM option?

LTX-2, from Lightricks, was announced in October 2025 and the full open-source release landed in January 2026. It is notable because it generates synchronized audio and video in a single pass at up to native 4K and 50fps, and it ships in two workflow flavors that matter here: a distilled "fast" tier for quick iteration and a full model for maximum quality (the model is also exposed through Fast / Pro / Ultra modes). The earlier LTX-Video line was already built for speed — the 0.9.7 generation was one of the first DiT video models capable of near real-time output, with a distilled FP8 13B build aimed at running on modest hardware.

For low-VRAM users the practical story is: the LTX family's distilled/fast tier is the lever, not the headline 4K spec. Generating at 4K locally is a high-end task; on a 6-8GB card you stay at lower resolutions and lean on the distilled model and few-step sampling (LTX-Video supports sampling in roughly 8 steps), which is what keeps it fast. People have reported running the older LTX-Video on as little as 6GB with quantized encoders and reduced resolution. If your goal is fast, low-fidelity iteration on weak hardware, the distilled LTX path is worth trying alongside Wan 2.2 5B; if your goal is the best single 720p clip on 8GB, Wan 2.2 5B is the more predictable pick today.

What can you realistically make on 6-8GB?

Set expectations correctly and low-VRAM video is genuinely useful; set them wrong and it is endless out-of-memory frustration. Here is the honest envelope, treating times as approximate and hardware-dependent.

Two realities to internalize. First, short means short. Local low-VRAM video means clips measured in a few seconds, not minutes of footage; you stitch clips together afterward. Second, resolution is your main VRAM dial. Going from 480p to 720p, or adding frames, is what tips a working 6-8GB workflow into OOM — change one variable at a time.

For our first-hand framing: on a mid-range 12GB card (RTX 3060-class), a short 720p Wan 2.2 5B FP8 clip is comfortably in the 5-12 minute range depending on steps and length, and the 14B GGUF at 480p on the same card runs but feels noticeably heavier. On a true 6-8GB card those times stretch further and 720p on the 14B becomes a patience exercise. Treat these as ballpark figures from typical community setups, not a controlled benchmark — your steps, sampler, frame count and storage speed move them substantially.

When should you use the cloud instead?

Local is the right call for privacy, zero per-clip cost, and unlimited tinkering. But be honest about the breakpoints where renting a GPU wins:

• You want 14B-class quality at 720p without 15-minute waits. A rented 24GB+ GPU runs the 14B at full precision and turns a coffee-break render into a quick one.
• You are iterating on prompts. If you are testing 50 variations, paying for a few hours of a cloud GPU is cheaper in time than babysitting OOM errors on 6GB.
• You only have CPU. CPU-only video generation works but is so slow it is impractical for anything but a one-off curiosity; this is the clearest "rent it" case.
• You need 4K or audio-synced output (e.g. the full LTX-2 path) — those are high-VRAM tasks by design.

A reasonable hybrid: prototype and learn locally on your 8GB card with Wan 2.2 5B, then do final/high-res renders in the cloud. To plan exactly which model fits your card and quant before you download tens of gigabytes, run the numbers in our 2026 VRAM requirements guide, and if you are also doing local image generation see the companion FLUX local image generation guide for the same low-VRAM mindset applied to stills.

Key Takeaways

1. Wan 2.2 TI2V-5B (FP8) is the best 8GB pick — a 5-second 720p clip with ComfyUI offloading enabled. Start here before fighting with the 14B.
2. Wan 2.2 14B GGUF (Q4/Q5) runs from 6GB by quantizing weights and offloading the T5 text encoder to system RAM — at 480p and noticeably slower (often 10-15+ min/clip).
3. You need 24GB+ system RAM, ideally 32GB. Offloading is what makes small GPUs work, and offloading lives in system RAM — under-buying RAM is the most common cause of low-VRAM crashes.
4. Use ComfyUI's --lowvram / --novram / --reserve-vram flags (or a wrapper like Wan2GP) and drop resolution and frame count first when you hit OOM.
5. CPU-only works but is impractically slow. For weak hardware the realistic order is 5B on 8GB, then 14B GGUF on 6GB, then the cloud for 720p/14B/4K at usable speed.

Next Steps

• Ready for the full quality path? Read the Wan video generation guide for the complete 5B and 14B workflows.
• Want a different model family? The HunyuanVideo guide covers another strong open text-to-video option.
• New to the tool? Start with the complete ComfyUI guide before wiring a low-VRAM video graph.
• Sizing a GPU or quant? See the 2026 VRAM requirements guide to match models to your card.
• Also doing stills on weak hardware? The FLUX local image generation guide applies the same low-VRAM playbook to images.