Alaya Lab releases Evoke, a 14B open world model with external scene memory for hour-long interaction · cho.sh
Alaya Lab releases Evoke, a 14B open world model with external scene memory for hour-long interaction
AI video model releases
Alaya Lab releases Evoke, a 14B open world model with external scene memory for hour-long interaction
Alaya Lab's Evoke is a 14-billion-parameter open-weights world model that stores generated scene geometry outside its video denoiser, indexed by camera pose, to maintain consistency across hour-long interactive sessions.
After each nine-frame, 1.5-second chunk, Evoke estimates depth, converts frames to 3D geometry, and retrieves up to eight overlapping stored views to condition the next generation.
The distilled model uses three denoising steps without classifier-free guidance and generates 1.5 seconds of 384 by 640 video in 2.11 seconds on one H200, so it remains slower than real-time playback.
Evoke publishes its full training ladder, including a 148 GB teacher and roughly 53 GB checkpoints for several stages; the usual distilled checkpoint plus shared text encoder and VAE takes about 75 GB on disk.
The inference weights alone occupy roughly 39 GB of VRAM before generation, while all published performance measurements use a 141 GB H200; Alaya Lab gives no minimum-memory specification.
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