I Built a Prompt Compressor That Saves 65% on LLM Costs — Here's the Story

#python #ai #llm #opensource

I've been working on a side project called SuperCompress — an intelligent prompt compression system for LLMs. The idea is simple: most tokens you send to an LLM never need to be processed. They're padding, boilerplate, irrelevant context. But they still burn GPU cycles.

I wanted to fix that.

The Problem

Working with LLM agents, I noticed something: every agent loop was sending massive context through the GPU. 10K tokens. 50K tokens. Sometimes more. Most of it was irrelevant to the specific task.

Truncation (keeping head + tail) was the standard approach, but it regularly dropped critical information from the middle of the context.

I thought: what if we could score each line of context for relevance BEFORE sending it to the GPU? A tiny CPU model that decides what matters.

The Build

The technical challenge was:

Train a lightweight policy (~5K params) that runs on CPU in under 60ms
Score each line of context relative to the user's question
Evict low-relevance lines while keeping answer-critical ones
Ensure the compressed output preserves correct answers

After a lot of iteration, the results surprised even me:

Policy	KV Saved	Oracle Recall
Truncation	65%	25%
H2O	65%	98%
SuperCompress	65%	100%

100% oracle recall at the same token savings. The policy never dropped a line the answer depended on.

The Environmental Angle

Here's what hit me hardest: at 50M agent turns per day (a conservative estimate for the industry), we're wasting 100B tokens daily. That's 24K GPU hours, 1,526 tons of CO₂, 6.5M liters of cooling water. Every day.

Per 1 million compressions, SuperCompress saves: