Making Knowledge Distillation Cheap Enough to Run at Scale
Reducing the computational cost of knowledge distillation makes it practical for organizations to create smaller, efficient models from larger ones.
Knowledge distillation is a machine learning technique where a smaller model is trained to match the performance of a much larger model, which matters because deploying very large language models requires enormous computing resources and memory. The distillation process itself has been prohibitively expensive, typically requiring hundreds of GPUs and careful memory management strategies because it must keep both the large teacher model and smaller student model loaded simultaneously while computing probability distributions across the entire vocabulary. Researchers have developed two system changes to reduce this cost: caching the teacher model's output once so it never needs to be loaded during training, and using a memory-efficient loss calculation method that processes data in chunks rather than building massive grids in memory all at once. These changes make it possible to run knowledge distillation on a single GPU and allow large-scale experimentation that was previously impractical.
One short email a day with the stories that matter. Free. Unsubscribe with one click.
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven
Anthropic releases new Mythos and Fable models amid RSI concerns
Anthropic says the new models "show improvements in areas like coding, reasoning, and vision," as well as in communicating more clearly with users.
Also reported by The Verge, SiliconANGLE and MarkTechPost
Google Launches Gemini Decisions API With Gemini Decision 1 at $0.10/M Tokens
Google has launched the Gemini Decisions API, powered by a new model, Gemini Decision 1 (gemini-decision-1). The Gemini Decisions API does not generate text. It scores up to 1,000 typed questions against one shared input and returns calibrated probabilities. For developers, this targets the small branching calls inside agents: routing, policy checks, tool selection and grading. Google prices it at $0.10 per 1M input tokens, with zero output tokens billed. TL;DR Size: Context is 262,144 t
Also reported by SiliconANGLE
Google releases Gemini 4 Carbon with agent ‘swarm’ feature
Google is rolling out its latest AI model, Gemini 4 Carbon, to the general public after releasing it to a select group of "cybersecurity defenders" two weeks ago. Google claims Carbon is its "strongest model" yet, and capable of competing with the very best systems from Anthropic and OpenAI. Google says Gemini 4 Carbon "pushes the frontier even further" than Argon, referring to the company's other frontier model that launched only last month. The company said Argon will likely
Gemini 4 Carbon is now available
The latest version aims to deliver improved performance while reducing the computational resources and energy required for AI operations.
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.
NVIDIA launches GR00T 2 and Isaac Lab 3.0 at CoRL
A range of developers, including RLWRLD, Skild AI, and Flexion, are already using NVIDIA’s latest technology. | Source: NVIDIA NVIDIA Corp. today introduced GR00T 2, its updated open frontier world action model, and Isaac Lab 3.0, the latest version of its open-source robot learning framework. The company made these announcements at the Conference on Robot Learning, or CoRL, in Austin, Texas. NVIDIA also gave a look at how its partners, like Ambi Robotics, Bosch Research, Dexmate, Dyna Rob