# SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference

arXiv: https://arxiv.org/abs/2606.00365 · HTML: https://mlsanity.com/paper/2606.00365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://mlsanity.com/paper/2404.00456/) (2024)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [Atom: Low-bit Quantization for Efficient and Accurate LLM Serving](https://mlsanity.com/paper/2310.19102/) (2023)
- [DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs](https://mlsanity.com/paper/2406.01721/) (2024)
- [GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM](https://mlsanity.com/paper/2403.05527/) (2024)
- [Training-Free Activation Sparsity in Large Language Models](https://mlsanity.com/paper/2408.14690/) (2024)
- [MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization](https://mlsanity.com/paper/2411.05282/) (2024)
- [BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference](https://mlsanity.com/paper/2501.01144/) (2025)
- [FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices](https://mlsanity.com/paper/2403.09026/) (2024)
- [TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction](https://mlsanity.com/paper/2410.19103/) (2024)
- [Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration](https://mlsanity.com/paper/2011.13000/) (2020)

## Abstract (arXiv)

The rapid growth in sizes of Large language models (LLMs) results in high compute and memory costs during inference. Quantization has been a significant pathway to addressing this challenge. In the quest to push the limits of quantization, weights, which are static, can often be quantized aggressively (e.g. 4 bits), while activations often require higher precision (e.g., 8 bits) to preserve accuracy, forcing hardware to operate with higher-precision datapaths. We leverage the statistical property that a significant fraction of activations are concentrated around zero, resulting in sparsity in the higher-order bits. Our proposal, SPARQLe, is a hardware-software co-design framework that exploits this sub-precision redundancy in any given quantized model. SPARQLe represents each 2k-bit activation tensor as a dense k-bit LSB tensor and a sparse k-bit MSB tensor compressed with a precision bitmap, and proposes a lightweight algorithm to increase MSB sparsity. SPARQLe reduces activation memory traffic and enables efficient computation on k-bit datapaths while preserving 2k-bit activation accuracy. SPARQLe includes an accelerator that operates directly on this hybrid format with minimal control overheads. Across the BitNet 3B, Llama2 7B, and Llama3 8B models, SPARQLe reduces prefill latency by 16-24.3% and decode latency by 13.5-23.4%, with 17-26.7% and 6.5-14.2% lower prefill and decode energy, respectively. SPARQLe demonstrates that sub-precision activation sparsity offers an effective and complementary pathway towards efficient LLM inference.