SuperCompress: Cut LLM Costs by 65% Without Losing Answers

A developer built SuperCompress, an open-source CPU policy that cuts 65% of tokens before LLM inference, reducing costs and environmental impact. The tool scores each line of context for relevance, evicting low-scoring tokens to save KV cache and compute while maintaining answer quality. At scale, it could save 800M tokens, 29 kWh, and 12 kg CO₂ per million compressions.

Every LLM call burns GPU cycles on tokens that never needed to run. Padding. Boilerplate. Irrelevant context. I built SuperCompress — a tiny CPU policy that cuts 65% of tokens before inference. Open source. MIT. Free tier. supercompress.vercel.app The problem is worse than most people realize. At ~50M agent turns/day: → 100B tokens wasted daily → 24K GPU hours → 1,526 tons CO₂ → 6.5M L cooling water We're burning through resources on tokens that don't matter. How it works: 1️⃣ Context + question → CPU policy 5K params 2️⃣ Every line scored for relevance to the question 3️⃣ Low-scoring lines evicted 4️⃣ Only essential tokens reach the GPU CPU first. GPU for what matters. The numbers at 35% budget: • 65% KV cache saved • 100% oracle recall vs 25% for truncation • ~60ms CPU latency Same answers. ⅓ the compute. Per 1 million compressions: → 800M tokens avoided → 29 kWh saved → 12 kg CO₂ avoided → 52 L cooling water saved Scale that across the industry and it's enormous. SuperCompress is: ✅ Open source MIT ✅ Free API tier ✅ Python library ✅ Browser demo no install ✅ Integration guides for OpenAI/LangChain Try it: supercompress.vercel.app GitHub: github.com/arjunkshah/supercompress Built this because I believe we can't scale AI by burning through what we have left. Smarter compute means more AI for everyone — without the environmental cost. Would love feedback from the community 🙏 Links: GitHub https://github.com/arjunkshah/supercompress | Live Demo https://supercompress.vercel.app | Interactive Tool https://supercompress.vercel.app/compare