--- title: Making Knowledge Distillation Cheap Enough to Run at Scale description: Multiverse Computing presents two systems-level optimizations that dramatically reduce the cost of knowledge distillation for LLMs. First, offline distillation... canonical: https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Making Knowledge Distillation Cheap Enough to Run at Scale | daily.dev og:description: Multiverse Computing presents two systems-level optimizations that dramatically reduce the cost of knowledge distillation for LLMs. First, offline distillation... og:url: https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu og:image: https://api.daily.dev/og/posts/xSsMbSiFu.png og:image:alt: Making Knowledge Distillation Cheap Enough to Run at Scale og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Making Knowledge Distillation Cheap Enough to Run at Scale **[Hugging Face](https://daily.dev/sources/huggingface)** · 8 min read · 10 upvotes · 0 comments ## Summary Multiverse Computing presents two systems-level optimizations that dramatically reduce the cost of knowledge distillation for LLMs. First, offline distillation caches the teacher model's top-100 logits per token position once, eliminating the need to keep the teacher in memory during training. Second, a fused chunked KL-divergence loss avoids materializing the full vocabulary × sequence-length matrix by processing one chunk at a time and recomputing during the backward pass. Together, these reduce peak VRAM from ~250GB to ~58GB at 8K context on a single H200. At 32K tokens, memory drops 15.6× versus the dense baseline, and distilling a GPT-OSS 20B model shrinks from four GPU nodes to one with ~5× faster step times. The resulting 3.2B student distilled from Llama 3.1 8B Instruct retains most teacher accuracy at less than half the parameter count. The chunked-loss implementation is open-sourced on GitHub. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How much VRAM does a fused chunked KL loss save compared to dense KL loss when distilling LLMs at long context lengths? At 32K tokens, peak memory drops from 85.2 GiB with dense KL loss to 5.45 GiB with the fully chunked loss, a 15.6x reduction. The dense loss fails outright at 64K tokens and beyond. At 256K tokens, the fully chunked loss uses 11.6 GiB versus 134.2 GiB for the next-best chunked variant, running about 3.3x faster per iteration. _Track memory-efficient training techniques like this on daily.dev before scaling your own distillation runs._ ### What is offline knowledge distillation and how does it differ from online distillation for training smaller LLMs? Offline distillation computes the teacher model's output once, caching the top-100 most likely tokens per position, so the teacher never has to stay loaded in memory during student training, unlike online distillation which recomputes the teacher every step. This lets the cache be reused across many experiments and, combined with a fused chunked KL loss, cuts peak memory from roughly 250GB to about 128GB for models like gpt-oss-120b. _Compare distillation approaches like this on daily.dev when deciding how to compress large models cheaply._ ### How much accuracy does a distilled 3.2B parameter student model retain compared to its 8B parameter teacher? A 3.2B parameter student distilled from Llama 3.1 8B Instruct retains most of the teacher's accuracy on BoolQ and HellaSwag benchmarks, staying within about nine points of the teacher on MMLU, at less than half the parameter count. This was made affordable through an efficient offline distillation setup with a memory-optimized KL loss. _Developers weighing model size against accuracy tradeoffs can follow distillation results like this on daily.dev._ ## Similar posts on daily.dev - [​Implement Knowledge Distillation from Scratch​](https://daily.dev/posts/implement-knowledge-distillation-from-scratch--vieqsxirg) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels](https://daily.dev/posts/cutting-llm-memory-by-84-a-deep-dive-into-fused-kernels-qa2yvt9ou) · Towards Data Science · 0 upvotes · 0 comments - [Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer](https://daily.dev/posts/pruning-and-distilling-llms-using-nvidia-tensorrt-model-optimizer-ujki8fjpw) · NVIDIA Developer · 3 upvotes · 0 comments - [A Gentle Introduction to Model Distillation](https://daily.dev/posts/a-gentle-introduction-to-model-distillation-mzqzy6iaj) · Machine Learning Mastery · 49 upvotes · 2 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#pytorch](https://daily.dev/tags/pytorch) [View this post on daily.dev](https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Making Knowledge Distillation Cheap Enough to Run at Scale","url":"https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu"},"datePublished":"2026-08-10T10:08:43.867Z","dateModified":"2026-09-14T06:42:03.433Z","description":"Multiverse Computing presents two systems-level optimizations that dramatically reduce the cost of knowledge distillation for LLMs. First, offline distillation...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ed186d4f212c512ddb23894d769e72a3?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ed186d4f212c512ddb23894d769e72a3?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Hugging Face","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Hugging Face","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/f1f55c67d81a4330acf5b90b26b0c8e1","url":"https://daily.dev/sources/huggingface"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":10},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm,pytorch","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Hugging Face","item":"https://daily.dev/sources/huggingface"},{"@type":"ListItem","position":3,"name":"Making Knowledge Distillation Cheap Enough to Run at Scale"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/making-knowledge-distillation-cheap-enough-to-run-at-scale-xssmbsifu#faq","mainEntity":[{"@type":"Question","name":"How much VRAM does a fused chunked KL loss save compared to dense KL loss when distilling LLMs at long context lengths?","acceptedAnswer":{"@type":"Answer","text":"At 32K tokens, peak memory drops from 85.2 GiB with dense KL loss to 5.45 GiB with the fully chunked loss, a 15.6x reduction. 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