VeizikNVIDIA and Apple Silicon builds are available now.Release notes →
← Models
Video · NVIDIA · CUDA

Wan 2.1 · Wan 2.2

Run the Wan2.1 and Wan2.2 text-to-video and image-to-video checkpoints in veizik's native runtime. Weights are user-supplied, not shipped.

Status
Available
Platform
NVIDIA · CUDA
Class
Video
Publisher
Wan-AI (Alibaba)
Architecture
Wan2.1 (text-to-video)
Weights
Supplied by you
Package

What this package does

From the documents of veizik-wan-1.2.8-linux-x86_64.vzpack (release v1.2.8).

  • Four video models in one package: Wan2.1-T2V-1.3B, Wan2.1-T2V-14B and Wan2.2-T2V-A14B for text to video, and Wan2.2-I2V-A14B for image to video.
  • Run: veizik run <model key> "prompt" --out DIR renders a video and writes its frames as PNG files to DIR/frames/. Options: --steps, --frames, --width, --height, --seed, --guidance, --guidance2, --shift, --mode, --neg-file, --image, --det-vae, --checkpoint-every and --png-step; --lora and --lora-scale are accepted, but LoRA adapters are not part of this release. Any other option is refused with <flag> does not apply to this model. Every run also takes --out, --name, --prompt-file, --resume and --resume-from. Nothing is written inside the package.
  • Serve: veizik serve wan22_t2v_a14b --port 8000 runs a resident render server — the model loads once and is reused across requests with no reload, in one process and one GPU context, and the resident server re-checks the licence on each request. POST /v1/videos/generations with {"prompt": "…", "seed": 42} renders and returns the folder of PNG frames; /health and /v1/models answer too. An optional "priority": "urgent" on a request interrupts a running render at a step boundary, saves its progress (.vzckpt), serves the urgent request, then resumes the first. A non-resident bundle renders one request at a time.
  • Image to video: Wan2.2-I2V-A14B takes its first frame with --image FILE, as raw RGB bytes (one byte per channel) at the render size.
  • Four modes: quality (the reference path and the default), balanced, fast and ultrafast. Only quality is bit-identical to the reference.
  • Memory, per model: Wan2.1-T2V-1.3B runs in about 7 GB of VRAM; the 14B models (Wan2.2-T2V-A14B and Wan2.2-I2V-A14B) render resident in about 18.5 GB on a 24 GB consumer card (peak near 22 GB for T2V-A14B). Host system memory stays around 2 GB (peak near 1.83 GB at the VAE decode of an 81-frame clip) — the weights stream to the GPU rather than loading into system RAM, so a 14B video model runs on an ordinary machine.
  • Resume: an interrupted render continues with --resume and the same --out (or --resume-from REF, where REF is a --name or an absolute run directory), and the result is bit-identical to a render that never stopped. Resume finishes the same render — same prompt, seed and settings — so to change the prompt, start a new render. --checkpoint-every N sets how often progress is saved.
  • Preemption: create DIR/work/preempt and the render saves its state at the next step boundary and exits 75; resume it as above.
  • Progress: DIR/work/progress always holds one JSON line (state, stage, step, window, frames committed, estimated time left), replaced atomically, so a program never has to parse logs.
  • Deterministic VAE: with --det-vae yes the same input gives byte-exact frames.
  • Provenance mark: every frame carries the v1 mark, an in-pixel watermark plus XMP DigitalSourceType trainedAlgorithmicMedia. It holds no prompt, device, path or account data.
  • Kernel sets for sm_80, sm_86, sm_89, sm_90a, sm_100a and sm_120a; the launcher picks the set for the GPU it finds. Every speed figure for this package was measured on an RTX 3090 only.
  • veizik install downloads the package from its public GitHub release and refuses it unless its SHA-256 matches the published release. Every file is checked against its sealed manifest before each run; a changed, added or missing file stops the run with exit code 4.
  • Activation is veizik activate, once per device. veizik pull wan then fetches the Wan weights and prepares the video decoder automatically (no manual VAE layout step); for weights you already have, veizik pull --prep-local DIR prepares them in place.
Guide

How to run it

  1. Get the veizik command

    Get the veizik command for Linux x86-64:

    veizik
    $ curl -fsSL https://veizik.com/install.sh | sh
    # if the installer asks you to put ~/.local/bin on your PATH
    $ export PATH="$HOME/.local/bin:$PATH"
  2. Install the model engine

    This model is in veizik-wan-1.2.8-linux-x86_64.vzpack. veizik install downloads it from its GitHub release and refuses it unless its SHA-256 matches the one on the download page.

    install
    # accepted names: wan, wan21, wan22 or a full model key (wan21_t2v_1_3b, wan21_t2v_14b, wan22_t2v_a14b, wan22_i2v_a14b); any case; '.' and '-' work too (wan2.2)
    $ veizik install wan22
  3. Activate this device

    Approve the machine once; it receives a licence bound to it.

    activate
    $ veizik activate
  4. Run it

    The first commands for this model, as the package’s own documents give them.

    run
    # Wan weights are user-supplied (Apache-2.0). veizik pull fetches them and prepares the video decoder automatically — no manual VAE setup, no support request:
    $ veizik pull wan
    # already downloaded the Wan weights? prepare them in place, offline, instead:
    $ veizik pull --prep-local /data/models/Wan2.2-T2V-A14B
    $ veizik run wan22_t2v_a14b "<your prompt>" --out ./clip
    # options: --steps --frames --width --height --seed --guidance --guidance2 --shift --mode --neg-file --det-vae
    $ veizik run wan22_t2v_a14b "<your prompt>" --steps 40 --frames 81 --width 1280 --height 720 --seed 42 --out ./clip
    # the frames are written to ./clip/frames/; interrupted? the same command with --resume continues it
    $ veizik run wan22_t2v_a14b "<your prompt>" --out ./clip --resume
    # serve: an HTTP render API on 127.0.0.1
    $ veizik serve wan22_t2v_a14b --port 8000
    $ curl -s http://127.0.0.1:8000/v1/videos/generations -H 'content-type: application/json' -d '{"prompt": "<your prompt>", "seed": 42}'
    $ curl -s http://127.0.0.1:8000/health
Support

Where it runs.

Evidence and availability are separate: a verified device is not a download, and an available model is not verified on every device.

VerifiedVerifiedRun end to end on this exact device, on a published build.Architecture-supportedArchitecture-supportedVerified on another device of the same architecture; this device itself is untested.Expected, untestedExpected, untestedThe engine targets this architecture; nobody has run this model on it. No claim is made.In preparationIn preparationNo build is downloadable yet, and no figure is offered.Not offeredNot offeredThis family is not offered on this platform.UnknownUnknownNo information yet — the platform is announced with the release.
FamilyRTX 3080 TiRTX 3090RTX 4090RTX 5090RTX A5000A40A100 80GB PCIeA100-SXM4-80GBH100 80GB HBM3RTX PRO 4500 BlackwellM4 ProM1 UltraM1 MaxM2 Pro
Wan 2.1 · Wan 2.2
all variants
Architecture-supportedVerifiedExpected, untestedExpected, untestedArchitecture-supportedArchitecture-supportedExpected, untestedExpected, untestedExpected, untestedExpected, untestedNot offeredNot offeredNot offeredNot offered

Availability by variant

Model idAvailabilityPlatformContextWeights in memory
wan21-t2v-1.3bAvailableNVIDIA——
wan21-t2v-14bAvailableNVIDIA——
wan22-t2v-a14bAvailableNVIDIA——
wan22-i2v-a14bAvailableNVIDIA——

Verified on

  • GeForce RTX 3090 · build cuda-wan-1.2.8 · 2026-10-28 · published measurement

Earlier-build evidence

On 2026-10-21 an earlier build of the Wan engine rendered all four models bit-exact against the package's own reference hashes (CONFORMANCE.json) on an NVIDIA RTX 3090; the shipped engine is a later rebuild and has not been re-rendered.

“Verified” means this family ran end to end on that exact device, on a published build. Architecture membership never upgrades a device to verified.

Variants

4 checkpoints

Model idSizeCheckpointOn diskContextWeights in memory
wan21-t2v-1.3b1.3BWan-AI/Wan2.1-T2V-1.3B-Diffusers———
wan21-t2v-14b14BWan-AI/Wan2.1-T2V-14B-Diffusers———
wan22-t2v-a14bA14BWan-AI/Wan2.2-T2V-A14B-Diffusers———
wan22-i2v-a14bA14BWan-AI/Wan2.2-I2V-A14B-Diffusers———
Performance

Our figures for this build

Each table shows the GPUs a model was measured on and one figure per measure: the default mode and the most common workload. Hover a figure for its build, date, runs and range; open All figures for every mode and workload. A figure marked earlier build is our own measurement of an earlier build of the engine, shown because the computation it exercises did not change in substance; it is replaced when the package you download is measured on that GPU.

Wan2.1-T2V-1.3B

earlier build Every figure for this model is from an earlier build; the package has not been measured yet.

GPUPer step s/stepRender sPeak memory GB
RTX 3080 Ti6.648balanced338.4balanced6.88balanced
RTX 4090—162.9balanced—
RTX 5090—118.1balanced—
A100-SXM4-80GB—192.1balanced—
All 23 figures for Wan2.1-T2V-1.3B
GPUMeasureModeWorkloadValueRunsBuildDate
RTX 3080 TiRenderbalanced832x480, 81 frames, 50 steps338.4 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderbalanced1280x720, 81 frames, 50 steps1,473.8 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderfast832x480, 81 frames, 50 steps258.8 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderfast1280x720, 81 frames, 50 steps1,025.5 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderquality832x480, 81 frames, 50 steps407.4 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderultrafast832x480, 81 frames, 50 steps178.8 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiRenderultrafast1280x720, 81 frames, 50 steps601.2 s1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepbalanced832x480, 81 frames, 50 steps6.648 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepbalanced1280x720, 81 frames, 50 steps29.1 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepfast832x480, 81 frames, 50 steps5.055 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepfast1280x720, 81 frames, 50 steps20.1 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepquality832x480, 81 frames, 50 steps8.027 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepquality1280x720, 81 frames, 50 steps35.9 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepultrafast832x480, 81 frames, 50 steps3.455 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPer stepultrafast1280x720, 81 frames, 50 steps11.6 s/step1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPeak memorybalanced832x480, 81 frames, 50 steps6.88 GB1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPeak memoryfast832x480, 81 frames, 50 steps6.88 GB1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPeak memoryquality832x480, 81 frames, 50 steps6.88 GB1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPeak memoryquality1280x720, 81 frames, 50 steps7.16 GB1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 3080 TiPeak memoryultrafast832x480, 81 frames, 50 steps6.88 GB1earlier build WAN 2.1 1.3B research build 056e44cb (2026-10-22)2026-10-22
RTX 4090Renderbalanced832x480, 81 frames, 50 steps162.9 s2earlier build WAN 2.1 1.3B development build (2026-10-14)2026-10-14
RTX 5090Renderbalanced832x480, 81 frames, 50 steps118.1 s2earlier build WAN 2.1 1.3B earlier build (2026-10-10)2026-10-10
A100-SXM4-80GBRenderbalanced832x480, 81 frames, 50 steps192.1 s2earlier build WAN 2.1 1.3B development build (2026-10-14)2026-10-14

Powered by Wan-AI/Alibaba.

Wan2.2-I2V-A14B

GPUPer step s/stepRender sPeak memory GB
RTX 309043.1quality350.9quality18.50quality
All 3 figures for Wan2.2-I2V-A14B
GPUMeasureModeWorkloadValueRunsBuildDate
RTX 3090Renderquality832x480, 81 frames, 8 steps (base Wan2.2-I2V-A14B, oracle config, no LoRA)350.9 s1cuda-wan-1.2.82026-10-28
RTX 3090Per stepquality832x480, 81 frames, 8 steps (base Wan2.2-I2V-A14B, oracle config, no LoRA)43.1 s/step1cuda-wan-1.2.82026-10-28
RTX 3090Peak memoryquality832x480, 81 frames, 8 steps (base Wan2.2-I2V-A14B, oracle config, no LoRA)18.50 GB1cuda-wan-1.2.82026-10-28

Powered by Wan-AI/Alibaba.

Wan2.2-T2V-A14B

GPUPer step s/stepRender sPeak memory GB
RTX 3090—72.1quality—
RTX 409032.4ultrafastearlier build1,303.7ultrafastearlier build22.19ultrafastearlier build
RTX 509026.1ultrafastearlier build1,050.0ultrafastearlier build22.19ultrafastearlier build
All 7 figures for Wan2.2-T2V-A14B
GPUMeasureModeWorkloadValueRunsBuildDate
RTX 3090Renderquality1280x720, 17 frames, 4 steps (base Wan2.2-T2V-A14B, MoE dual-expert, quality mode, no LoRA)72.1 s1cuda-wan-1.2.82026-10-28
RTX 4090Renderultrafast1280x720, 81 frames, 40 steps1,303.7 s1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20
RTX 4090Per stepultrafast1280x720, 81 frames, 40 steps32.4 s/step1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20
RTX 4090Peak memoryultrafast1280x720, 81 frames, 40 steps22.19 GB1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20
RTX 5090Renderultrafast1280x720, 81 frames, 40 steps1,050.0 s1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20
RTX 5090Per stepultrafast1280x720, 81 frames, 40 steps26.1 s/step1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20
RTX 5090Peak memoryultrafast1280x720, 81 frames, 40 steps22.19 GB1earlier build WAN 2.2 A14B development build (2026-10-20)2026-10-20

Powered by Wan-AI/Alibaba.

Measurement conditions

Dedicated single-GPU cloud host; one clean render; board power as recorded in the evidence file. NVIDIA A100-SXM4-80GB; 400 W limit, mean draw 387.65 W; driver 580.159.04; dedicated single-GPU cloud host; two runs. NVIDIA GeForce RTX 3080 Ti (12 GB), dedicated to the run, 400 W limit, driver 591.86; 1 run(s). NVIDIA GeForce RTX 4090; 450 W limit, mean draw 446.95 W; driver 580.126.20; dedicated single-GPU cloud host; two runs. NVIDIA GeForce RTX 5090; 575 W, uncapped; driver 570.195.03; dedicated single-GPU cloud host; two runs. Native RTX 3090 (sm_86), non-root; sealed 1.2.8; int8; one clean render, SHARP (final-latent variance 0.468). Render-only compute (excludes weight cold-load and expert-swap). Base Wan2.2-I2V-A14B 8-step, NOT the distilled Lightning path. Customer-runnable via `veizik pull/run wan22_i2v_a14b --image`. Native RTX 3090 (sm_86), non-root; sealed 1.2.8; one clean render, coherent (final-latent var 0.848, nonfinite=0, rc=0, 17 PNGs); F32 umT5 auto-substituted. Base Wan2.2-T2V-A14B 4-step quality, NOT the distilled Lightning path. Render-only compute (excludes weight cold-load and expert-swap). Peak VRAM + per-step pending a later DEPLOY_MEASURE run. Customer-runnable via `veizik pull/run wan22_t2v_a14b`.

Licence

Weights and terms

Veizik ships no third-party model weights; you supply them. Wan 2.1 and Wan 2.2 models (Wan-AI, Alibaba) — Wan2.1-T2V-1.3B, Wan2.1-T2V-14B, Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, with their text encoder, tokenizer and VAE — are licensed under the Apache License 2.0 (https://huggingface.co/Wan-AI, https://github.com/Wan-Video). Their licence terms, including the acceptable-use terms of the model cards, apply to your use of the weights and of what you generate with them.

Full licence terms →

Other models linked in the sidebar:

Stable Diffusion 3.5: Powered by Stability AI HunyuanImage: Weights are not included. Model availability and use are governed by the model provider's license and regional restrictions.