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FreeToken Unlocks Frontier MoE Inference on Consumer Hardware via Dynamic Co-Execution

Researchers from UC Berkeley and MIT have developed FreeToken, an open-source inference engine that enhances the utility of Mixture-of-Experts models on consumer hardware. By implementing a dynamic scheduling policy and optimising weight management, FreeToken improves decoding speeds and execution efficiency in edge AI applications, fostering self-hosted reasoning systems. By Olimpiu Pop

We haven't written up this one. InfoQ has the full story — the link below goes straight to it.

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Object‑agnostic planners grasp unseen objects zero‑shot

Grasp planners that hinge on object models are being eclipsed by approaches that synthesize contacts directly on the hand.

  • Generative planners infer contact surfaces on-the-fly for unfamiliar objects.
  • GOAG achieved 86.93% success on MultiDex without object-specific data.
  • CoToGrasp sets new standard on DexGraspNet with object-agnostic learning.

Why Does a 125B AI Model Use Only 6B Parameters at a Time?

Why Does a 125B AI Model Use Only 6B Parameters at a Time? A new AI model launches. You open X and immediately see numbers like: 125B parameters 6B active parameters 262K context Open weights And if…

  • Model has 125 billion parameters but activates only 6 billion at once
  • Mixture of Experts (MoE) architecture divides parameters into experts
  • Router selects relevant experts for each input token, optimizing resource use

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