MEby Meta FAIR-Edge
V-JEPA-AC-Edge
22%Action-conditioned V-JEPA for world-model planning at the edge.
Vision-Language-ActionMITINT8FP16world-modelplanning
23K downloads 480 deploymentsUpdated Mar 11, 2028
Headline:18.4ms · NVIDIA Jetson Thor · MIXED
About this model
Action-conditioned V-JEPA for world-model planning at the edge.
Authored by meta-fair-edge. Curated into the Fo’c’sle reference set on 2028-03-11. All cross-chip benchmarks below were collected in matched-pair runs in the HIL lab using the same input pipeline, same upstream preprocessing, and the same downstream consumer. See the methodology page for the full protocol.
- Task
- Vision-Language-Action
- Parameters
- 1.8 B
- Benchmarked on
- 3 chips
- Deployments
- 480
Architecture
Vision-Language-Action policy
Inferred from upstream weights · simplified
Headline benchmarks
Training data
Pretrained on the upstream maintainer’s released checkpoint. Edge-distillation pass uses 2.4M frames from the Fo’c’sle distillation corpus (consented public data + opt-in publisher contributions). Quantization-aware fine-tune uses 320K calibration samples drawn from the target task’s eval domain.
- Pretraining corpus: upstream maintainer release
- Distillation corpus: 2,400,000 frames
- Calibration set: 320,000 samples (per task)
- Eval set: standard benchmark + matched-pair HIL runs