{"slug": "supercompress-cut-llm-costs-by-65-without-losing-answers", "title": "SuperCompress: Cut LLM Costs by 65% Without Losing Answers", "summary": "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.", "body_md": "Every LLM call burns GPU cycles on tokens that never needed to run.\n\nPadding. Boilerplate. Irrelevant context.\n\nI built SuperCompress — a tiny CPU policy that cuts 65% of tokens before inference.\n\nOpen source. MIT. Free tier.\n\nsupercompress.vercel.app\n\nThe problem is worse than most people realize.\n\nAt ~50M agent turns/day:\n\n→ 100B tokens wasted daily\n\n→ 24K GPU hours\n\n→ 1,526 tons CO₂\n\n→ 6.5M L cooling water\n\nWe're burning through resources on tokens that don't matter.\n\nHow it works:\n\n1️⃣ Context + question → CPU policy (5K params)\n\n2️⃣ Every line scored for relevance to the question\n\n3️⃣ Low-scoring lines evicted\n\n4️⃣ Only essential tokens reach the GPU\n\nCPU first. GPU for what matters.\n\nThe numbers at 35% budget:\n\n• 65% KV cache saved\n\n• 100% oracle recall (vs 25% for truncation)\n\n• ~60ms CPU latency\n\nSame answers. ⅓ the compute.\n\nPer 1 million compressions:\n\n→ 800M tokens avoided\n\n→ 29 kWh saved\n\n→ 12 kg CO₂ avoided\n\n→ 52 L cooling water saved\n\nScale that across the industry and it's enormous.\n\nSuperCompress is:\n\n✅ Open source (MIT)\n\n✅ Free API tier\n\n✅ Python library\n\n✅ Browser demo (no install)\n\n✅ Integration guides for OpenAI/LangChain\n\nTry it: supercompress.vercel.app\n\nGitHub: github.com/arjunkshah/supercompress\n\nBuilt this because I believe we can't scale AI by burning through what we have left.\n\nSmarter compute means more AI for everyone — without the environmental cost.\n\nWould love feedback from the community 🙏\n\n**Links:** [GitHub](https://github.com/arjunkshah/supercompress) | [Live Demo](https://supercompress.vercel.app) | [Interactive Tool](https://supercompress.vercel.app/compare)", "url": "https://wpnews.pro/news/supercompress-cut-llm-costs-by-65-without-losing-answers", "canonical_source": "https://dev.to/arjunkshah/supercompress-cut-llm-costs-by-65-without-losing-answers-2c8n", "published_at": "2026-06-26 19:23:33+00:00", "updated_at": "2026-06-26 20:04:13.889992+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-ethics", "developer-tools", "machine-learning"], "entities": ["SuperCompress", "Arjun Shah", "OpenAI", "LangChain", "GitHub", "Vercel"], "alternates": {"html": "https://wpnews.pro/news/supercompress-cut-llm-costs-by-65-without-losing-answers", "markdown": "https://wpnews.pro/news/supercompress-cut-llm-costs-by-65-without-losing-answers.md", "text": "https://wpnews.pro/news/supercompress-cut-llm-costs-by-65-without-losing-answers.txt", "jsonld": "https://wpnews.pro/news/supercompress-cut-llm-costs-by-65-without-losing-answers.jsonld"}}