# Accuracy and Consumption analysis from a compressed model by CompactifAI from Multiverse Computing

arXiv: https://arxiv.org/abs/2507.08836 · HTML: https://mlsanity.com/paper/2507.08836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 1 of 1)

- [CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2401.14109/) (2024)

## Built on by (most cited first, 2 of 2)

- [Scaling Laws for Energy Efficiency of Local LLMs](https://mlsanity.com/paper/2512.16531/) (2025)
- [Activation-Aware Weight Tensorization: A Calibration-Time Preconditioner for Tensor-Network LLM Compression](https://mlsanity.com/paper/2610.10085/) (2026)

## Abstract (arXiv)

This study evaluates the performance of a compression method, called CompactifAI, developed by Multiverse Computing, applied to the large language model Llama 3.1 8Bcitellama. The evaluation focused on model efficiency (in terms of energy consumption) and accuracy using respectively the frameworks Codecarboncitecodecarbon and Ragasciteragas. A comparison was performed between the model compressed with CompactifAIcitecompactifaicitecompactifai2 and its full-size version. Our findings reveal that the compressed model using CompactifAI not only significantly reduced the computational resources but also maintained the model accuracy, making the model more efficient, scalable and cost-effective.