Model reference · open weights
Evoke is an open-weight video model from AlayaLab. Evoke-Turbo (FP32) weighs 28.6 GB; the smallest configuration that runs it is RTX 5090 32 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the AlayaLab/Evoke-Turbo model card, 2026-10-01
What it is
| Released by | AlayaLab |
|---|---|
| Released | 2026-09-11 |
| Parameters | 14.3B |
| VRAM | 28.6 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 4090 24 GB | does not fit | |
| RTX 5090 32 GB | tight | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
Alaya-EVOKE-Turbo is a controllability-optimized version of Alaya-EVOKE, focusing on camera-motion following and scene/subject instruction adherence. It uses the same architecture and inference workflow: 3 steps, no CFG, 384 × 640 at 24 fps.
Merged Turbo transformer weights, stored in FP32 to match Evoke. Download the shared base components and depth backend separately:
models/
├── evoke-base/ # VAE / text encoder / tokenizer / scheduler; from Evoke
├── evoke-turbo/transformer/ # this release
└── ViGeo1.1/ # required depth backend
git clone https://github.com/AlayaLab/Evoke && cd Evoke
pip install -r requirements.txt
hf download AlayaLab/Evoke --include "evoke-base/*" --local-dir models
hf download AlayaLab/Evoke-Turbo --local-dir models/evoke-turbo/transformer
hf download pkqbajng/ViGeo --local-dir models/ViGeo1.1 # REQUIRED depth backend
TRANSFORMER_PATH=models/evoke-turbo MODE=t2v NUM_CHUNKS=20 \
bash scripts/inference/infer_post_distill.sh
See Evoke for camera control, other inference modes, and long rollouts.
Results on WBench (158 cases; Interaction: Navigation only). Bold marks the best score in each column.
| Model | Video Quality | Setting | Navigation | Consistency | Physical | Avg |
|---|---|---|---|---|---|---|
| Evoke | 82.7900 | 83.7600 | 78.6300 | 86.8700 | 72.0550 | 80.8210 |
| Evoke-Turbo | 81.8914 | 82.0518 | 83.8978 | 88.1469 | 74.0133 | 82.0003 |
Turbo uses seed-44 results selected from six prompt variants; Evoke uses published reference scores, with different prompts and poses.
See Evoke for base-component provenance and dependency licenses; external components retain their own licenses.
@article{evoke2026,
title = {Alaya-EVOKE: From Linear-Scaling Supervision to Endless World},
author = {Yin, Yuanyang and Wang, Gongxuan and Zhan, Yifan and
Li, Chuanhao and Zhang, Kaipeng and Zhao, Feng},
journal = {arXiv preprint arXiv:2608.13546},
year = {2026},
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel and load the workflow from the model's card on Hugging Face; choose this model's file in its loader.
# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the model (7.2 GB)
get AlayaLab/Evoke-Turbo diffusion_pytorch_model-00006-of-00006.safetensors diffusion_models
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST, PORT and BOOKING are your machine's, from its page in the console
ssh -o HostKeyAlias=axforge-BOOKING -o StrictHostKeyChecking=accept-new -L 8188:localhost:8188 dev@HOST -p PORT