# Q-Sparse: All Large Language Models can be Fully Sparsely-Activated

arXiv: https://arxiv.org/abs/2407.10969 · HTML: https://mlsanity.com/paper/2407.10969/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning](https://mlsanity.com/paper/2310.06694/) (2023)
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://mlsanity.com/paper/2402.17764/) (2024)
- [Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://mlsanity.com/paper/2310.17157/) (2023)
- [BitNet: Scaling 1-bit Transformers for Large Language Models](https://mlsanity.com/paper/2310.11453/) (2023)
- [Primer: Searching for Efficient Transformers for Language Modeling](https://mlsanity.com/paper/2109.08668/) (2021)
- [You Only Cache Once: Decoder-Decoder Architectures for Language Models](https://mlsanity.com/paper/2405.05254/) (2024)
- [MiniLLM: On-Policy Distillation of Large Language Models](https://mlsanity.com/paper/2306.08543/) (2023)
- [ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models](https://mlsanity.com/paper/2310.04564/) (2023)
- [Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering](https://mlsanity.com/paper/1911.07176/) (2019)
- [Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters](https://mlsanity.com/paper/2406.05955/) (2024)

## Built on by (most cited first, 25 of 25)

- [Training-Free Activation Sparsity in Large Language Models](https://mlsanity.com/paper/2408.14690/) (2024)
- [MoH: Multi-Head Attention as Mixture-of-Head Attention](https://mlsanity.com/paper/2410.11842/) (2024)
- [BitNet b1.58 2B4T Technical Report](https://mlsanity.com/paper/2504.12285/) (2025)
- [Bidirectional Sparse Attention for Faster Video Diffusion Training](https://mlsanity.com/paper/2509.01085/) (2025)
- [BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs](https://mlsanity.com/paper/2504.18415/) (2025)
- [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://mlsanity.com/paper/2411.04965/) (2024)
- [Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations](https://mlsanity.com/paper/2504.06792/) (2025)
- [La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse Activation](https://mlsanity.com/paper/2507.01299/) (2025)
- [CSRv2: Unlocking Ultra-Sparse Embeddings](https://mlsanity.com/paper/2602.05735/) (2026)
- [Neuralink: Fast LLM Inference on Smartphones with Neuron Co-Activation Linking](https://mlsanity.com/paper/2410.19274/) (2024)
- [Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash](https://mlsanity.com/paper/2504.08378/) (2025)
- [Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity](https://mlsanity.com/paper/2505.07239/) (2025)
- [WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference](https://mlsanity.com/paper/2505.19427/) (2025)
- [SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models](https://mlsanity.com/paper/2503.07605/) (2025)
- [Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models](https://mlsanity.com/paper/2508.02128/) (2025)
- [Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches](https://mlsanity.com/paper/2509.22166/) (2025)
- [WiSparse: Boosting LLM Inference Efficiency with Weight-Aware Mixed Activation Sparsity](https://mlsanity.com/paper/2602.14452/) (2026)
- [MVDRAM: Enabling GeMV Execution in Unmodified DRAM for Low-Bit LLM Acceleration](https://mlsanity.com/paper/2503.23817/) (2025)
- [TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition](https://mlsanity.com/paper/2505.16447/) (2025)
- [Generalizing Scaling Laws for Dense and Sparse Large Language Models](https://mlsanity.com/paper/2508.06617/) (2025)
- [From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models](https://mlsanity.com/paper/2603.04828/) (2026)
- [Sparser, Faster, Lighter Transformer Language Models](https://mlsanity.com/paper/2603.23198/) (2026)
- [RT-Lynx: Putting GEMM Sparsity in the Right Place for Diffusion Models](https://mlsanity.com/paper/2605.26632/) (2026)
- [Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware](https://mlsanity.com/paper/2608.30439/) (2026)
- [Where Activation Sparsity and KV-Cache Sparsity Cross in LLM Decoding](https://mlsanity.com/paper/2609.33889/) (2026)

## Abstract (arXiv)

We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can bring significant efficiency gains in inference. This is achieved by applying top-K sparsification to the activations and the straight-through-estimator to the training. We also introduce Block Q-Sparse for batch training and inference. The key results from this work are, (1) Q-Sparse can achieve results comparable to those of baseline LLMs while being much more efficient at inference time; (2) We present an inference-optimal scaling law for sparsely-activated LLMs; (3) Q-Sparse is effective in different settings, including training-from-scratch, continue-training of off-the-shelf LLMs, and finetuning; (4) Q-Sparse works for both full-precision and 1-bit LLMs (e.g., BitNet b1.58). Particularly, the synergy of BitNet b1.58 and Q-Sparse (can be equipped with MoE) provides the cornerstone and a clear path to revolutionize the efficiency, including cost and energy consumption, of future LLMs.