Tools
Parcae: Doing more with fewer parameters using stable looped models
Parcae is a stable looped language model that matches the quality of a Transformer twice its size — a 770M model reaching 1.3B-level performance. We introduce the first scaling laws for looping and sh
Parcae is a stable looped language model that matches the quality of a Transformer twice its size — a 770M model reaching 1.3B-level performance. We introduce the first scaling laws for looping and show that increasing recurrence, not just data, is a compute-efficient path to bet
Related
- The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, no…
- What if you could get 1.3B Transformer quality from a 770M model? That's not a compression result. It's a different architecture. Parcae, fr…
- Parcae: Scaling Laws For Stable Looped Language Models
- Training code and models are live on Hugging Face. Dan Fu (Together AI's VP of Kernels) led the work. Together AI provided compute. Blog: ht…
Source: Together AI Blog | 2026-04-15