NVIDIA AI Releases Star Elastic: One Checkpoint that Contains 30B, 23B, and 12B Reasoning Models with Zero-Shot Slicing
360× token reduction. NVIDIA's Star Elastic embeds three reasoning models in one checkpoint—30B, 23B, and 12B—eliminating redundant training entirely.
Why it matters
NVIDIA demonstrates a fundamental shift in model scaling efficiency: instead of training separate variants, one post-training method now produces multiple nested models from a single run. This directly impacts inference cost and deployment flexibility for enterprises choosing between model sizes.
The key facts
6 to knowStar Elastic enables 30B, 23B, and 12B models in single checkpoint via zero-shot slicing
360× token reduction vs. training each model separately (trained on 160B tokens total)
Elastic budget control achieves 16% higher accuracy and 1.9× lower latency vs. standard budget control
Nested FP8 and NVFP4 checkpoints bring full model family within reach of RTX-class GPUs
Built on Nemotron Elastic framework, applied to Nemotron Nano v3
Post-training method, not architecture change
Go to the source
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Publisher excerpt: NVIDIA researchers have introduced Star Elastic, a post-training method that embeds multiple nested reasoning models — at 30B, 23B, and 12B parameter scales — inside a single checkpoint, eliminating the need for separate training runs or stored model weights per variant. Built on the Nemotron…