# QTIP: Quantization with Trellises and Incoherence Processing

arXiv: https://arxiv.org/abs/2406.11235 · HTML: https://mlsanity.com/paper/2406.11235/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://mlsanity.com/paper/2404.00456/) (2024)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models](https://mlsanity.com/paper/2308.13137/) (2023)
- [QuIP: 2-Bit Quantization of Large Language Models With Guarantees](https://mlsanity.com/paper/2307.13304/) (2023)
- [The case for 4-bit precision: k-bit Inference Scaling Laws](https://mlsanity.com/paper/2212.09720/) (2022)
- [Extreme Compression of Large Language Models via Additive Quantization](https://mlsanity.com/paper/2401.06118/) (2024)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [GPTVQ: The Blessing of Dimensionality for LLM Quantization](https://mlsanity.com/paper/2402.15319/) (2024)
- [PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression](https://mlsanity.com/paper/2405.14852/) (2024)

## Built on by (most cited first, 30 of 91)

- [GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance](https://mlsanity.com/paper/2505.07004/) (2025)
- [Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization](https://mlsanity.com/paper/2509.23202/) (2025)
- [A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms](https://mlsanity.com/paper/2409.16694/) (2024)
- [Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization](https://mlsanity.com/paper/2505.11695/) (2025)
- [Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models](https://mlsanity.com/paper/2501.19392/) (2025)
- [NestQuant: Nested Lattice Quantization for Matrix Products and LLMs](https://mlsanity.com/paper/2502.09720/) (2025)
- [Model-Preserving Adaptive Rounding](https://mlsanity.com/paper/2505.22988/) (2025)
- [The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm](https://mlsanity.com/paper/2507.18553/) (2025)
- [Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging](https://mlsanity.com/paper/2510.20639/) (2025)
- [ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference](https://mlsanity.com/paper/2511.10645/) (2025)
- [MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design](https://mlsanity.com/paper/2412.14590/) (2024)
- [WUSH: Near-Optimal Adaptive Transforms for LLM Quantization](https://mlsanity.com/paper/2512.00956/) (2025)
- [Optimal Quantization for Matrix Multiplication](https://mlsanity.com/paper/2410.13780/) (2024)
- [Identifying Sensitive Weights via Post-quantization Integral](https://mlsanity.com/paper/2503.01901/) (2025)
- [ICQuant: Index Coding enables Low-bit LLM Quantization](https://mlsanity.com/paper/2505.00850/) (2025)
- [PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models](https://mlsanity.com/paper/2509.16989/) (2025)
- [Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment](https://mlsanity.com/paper/2509.20214/) (2025)
- [DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization](https://mlsanity.com/paper/2604.17789/) (2026)
- [ActQuant: Sub-4-bit Action-Guided Quantization for Vision-Language-Action Models](https://mlsanity.com/paper/2605.24011/) (2026)
- [PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling](https://mlsanity.com/paper/2506.05432/) (2025)
- [WaterSIC: Information-Theoretically (Near) Optimal Linear Layer Quantization](https://mlsanity.com/paper/2603.04956/) (2026)
- [Leech Lattice Vector Quantization for Efficient LLM Compression](https://mlsanity.com/paper/2603.11021/) (2026)
- [DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration](https://mlsanity.com/paper/2505.23049/) (2025)
- [MACKO: Sparse Matrix-Vector Multiplication for Low Sparsity](https://mlsanity.com/paper/2511.13061/) (2025)
- [GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling](https://mlsanity.com/paper/2604.18556/) (2026)
- [Statistically-Lossless Quantization of Large Language Models](https://mlsanity.com/paper/2605.02404/) (2026)
- [Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven](https://mlsanity.com/paper/2605.06014/) (2026)
- [any4: Learned 4-bit Numeric Representation for LLMs](https://mlsanity.com/paper/2507.04610/) (2025)
- [Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://mlsanity.com/paper/2508.03332/) (2025)
- [HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization](https://mlsanity.com/paper/2605.29843/) (2026)

## Abstract (arXiv)

Post-training quantization (PTQ) reduces the memory footprint of LLMs by quantizing weights to low-precision datatypes. Since LLM inference is usually memory-bound, PTQ methods can improve inference throughput. Recent state-of-the-art PTQ approaches use vector quantization (VQ) to quantize multiple weights at once, which improves information utilization through better shaping. However, VQ requires a codebook with size exponential in the dimension. This limits current VQ-based PTQ works to low VQ dimensions (≤ 8) that in turn limit quantization quality. Here, we introduce QTIP, which instead uses trellis coded quantization (TCQ) to achieve ultra-high-dimensional quantization. TCQ uses a stateful decoder that separates the codebook size from the bitrate and effective dimension. QTIP introduces a spectrum of lookup-only to computed lookup-free trellis codes designed for a hardware-efficient "bitshift" trellis structure; these codes achieve state-of-the-art results in both quantization quality and inference speed.