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Wan 2.2 T2V A14B

safetensors (as published) · Wan-AI/Wan2.2-T2V-A14B

queued

PublisherWan-AI
Modalitytext-to-video
Parametersunknown
Card: MoE video diffusion (high-noise + low-noise experts, A14B), 5 s clips at 480P/720P. Total params not stated: unknown. Source: model card.
Formatsafetensors (as published)
Size on disk117.53 GiB (126,201,624,837 bytes)
Estimated memory to run~130.2 GB (estimate; see method)
Card's single-GPU command uses --offload_model True --convert_model_dtype; high-noise and low-noise experts are used one after the other, so peak memory is well below the repo size.
Native contextn/a
EngineWan2.2 generate.py / ComfyUI · recipe
Licenseapache-2.0
Apache 2.0: commercial OK.
Archive folderusb-media/video
NotesCard: MoE video diffusion (high-noise + low-noise experts, A14B), 5 s clips at 480P/720P. Total params not stated: unknown.

Where it runs

MachineVerdictBudget used
Bosgame M5 (x1)Tight / offload~108-110 GB GPU-usable on Linux with GTT raised (96 GB cap on Windows). Fit budget used here: 108 GB.
Bosgame M5 (x2, RPC)Tight / offload~200-225 GB usable across two nodes. Fit budget used here: 210 GB; 210-225 GB = tight.
MacBook Pro (M4 Max)NomacOS caps GPU wired memory by default (~75% = ~27 GB). Raising iogpu.wired_limit_mb to ~32 GB is possible but leaves little for the OS.
Mac mini (M4)No~2/3 of 16 GB usable by the GPU by default. Already busy as build box + Hermes/Ollama.
omarchy (AMD desktop)No7 GB of the 8 GB VRAM for weights. Offload capacity depends on system RAM, which is unknown today; after the 128 GB upgrade ~110 GB of MoE experts could sit in RAM.
Ugreen DXP2800 NASNoStorage host. Already runs ollama/open-webui containers; only tiny models (e.g. Whisper on CPU) are realistic.
MacBook NeoUnknownSpecs unknown: machine was offline/unreachable on Nov 14 2026.

Token/s = theoretical ceiling from memory bandwidth and active weight bytes; not a benchmark. Nothing on this page is a benchmark score.