video model · wan · Windows

Can I run Wan 2.2 T2V A14B on AMD Ryzen AI Halo (128GB)?

Compatibility verdict VRAM check

Yes, it runs fast on this GPU

Yes. Wan 2.2 T2V A14B runs on AMD Ryzen AI Halo (128GB) at Q4 GGUF (~16 GB of ~96 GB usable).

Needs ~16 GB Device usable ~96 GB

Runs at Q4 GGUF using ~16 GB of ~96 GB usable.

Peak VRAM: ~16 GB
Usable on device: ~96 GB
Device memory: 128 GB
Quant: Q4 GGUF

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How to run it

Use ComfyUI or Diffusers at Q4 GGUF. The big text encoder is loaded to encode your prompt, then offloaded before generation, which is why peak VRAM stays near the backbone size rather than the sum of every file.

Model wan

Type: video (DIT)
Parameters: 27B (MoE, 14B active)
Peak VRAM: ~16 GB at Q4 GGUF
Resolution: 1280×720 (720p)
License: Apache-2.0

Full Wan 2.2 T2V A14B requirements →

Device Windows

Memory: 128 GB unified
Usable for weights: ~96 GB
Power draw: ~120 W
Best runtime: llama.cpp (Vulkan/ROCm) / LM Studio

Best models for AMD Ryzen AI Halo (128GB) →

You could also run

Run Wan 2.2 T2V A14B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)

FAQ

Can AMD Ryzen AI Halo (128GB) run Wan 2.2 T2V A14B?

Yes. Wan 2.2 T2V A14B runs on AMD Ryzen AI Halo (128GB) at Q4 GGUF (~16 GB of ~96 GB usable).

How much VRAM does Wan 2.2 T2V A14B need?

AMD Ryzen AI Halo (128GB) has room to spare. At Q4 GGUF the realistic peak is ~16 GB of VRAM, versus ~80 GB with every component kept resident (no offload). With aggressive CPU offload it drops to ~8 GB, much slower.

What do I use to run Wan 2.2 T2V A14B locally?

Wan 2.2 T2V A14B runs in ComfyUI or Diffusers. It loads as a video diffusion checkpoint plus its text encoder and VAE, not a single chat command.

Sources

VRAM figures are sourced peak-usage anchors at the noted quant, validated 2026-08-03. See methodology.