Latest News & Insights
Updates, research papers, and analysis from the Condense Labs team.
Why LLM Distillation Feels Like Magic
LLM distillation compresses massive language models into tiny versions that punch far above their weight. This article explains why distillation works so well, how Chain of Thought distillation transfers reasoning ability, and why companies are using it to deploy powerful AI on edge devices at a fraction of the cost.
Why On-Device LLMs Are the Future — And How Condense Labs Makes It Possible
Cloud LLMs are expensive, slow, and create privacy risks. On-device models eliminate all three problems — but only if they're small enough to fit. This article explains why on-device LLMs are the future of AI deployment and how Condense Labs compresses large models by 40-100x using Chain-of-Thought distillation, structured pruning, and INT4 quantization. The result: models that run on phones, laptops, and edge devices while maintaining near-original performance.
How to Deploy Smart LLMs on Any Device — From Phones to Edge Devices
Deploying powerful AI models has always meant expensive infrastructure and cloud dependencies — until now. This article explains how Chain-of-Thought distillation, structured pruning, and INT4 quantization can compress any LLM by 40-100x while actually IMPROVING its performance on specific tasks. Learn how small language models running locally on phones, tablets, and edge devices outperform massive cloud APIs while cutting costs by 1,500x. The future of AI is local, and it's available right now.
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Why Your LLM is Too Expensive to Deploy (And What to Do About It)
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