Tools
The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, no…
The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, not by adding parameters. Bigger-model quality at smaller-mode
The edge inference implication: memory, not compute, is the binding constraint. Parcae opens a new axis. Scale quality by looping deeper, not by adding parameters. Bigger-model quality at smaller-model memory cost.
Related
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- Parcae: Doing more with fewer parameters using stable looped models
- I'm releasing the 34 slides on how we design and train best-in-class edge models at @liquidai I presented these slides yesterday at @aiDotEn…
Source: Together AI (X) | 2026-04-15