mlsanityData to 13 Oct 2026

High-Rate Quantized Matrix Multiplication I

arXiv 2601.17187 ↗First posted 23 Jan 2026cs.IT cs.AI math.ITcited by 7

Not on the map. Why?

Abstract (arXiv)

This paper investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of large language models (LLMs). We consider a Generic MatMul setting, where both matrices must be quantized (weight+activation quantization) without specific apriori (calibration) statistical information about the factors. We review the fundamental information-theoretic tradeoff between quantization rate and distortion (high-rate theory), and contrast those with the performance of popular quantization schemes (absmax INT and floating-point (FP)), for which we also derive accurate heuristic approximations. Part II of this paper studies the weight-only quantization setup where second-order statistics of the activation matrices are available at the encoder.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 23 Jan 2026.

Builds on

13 of 31 references.

References · 13

  1. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,365
  2. 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMscited by 614
  3. 2024SpinQuant: LLM quantization with learned rotationscited by 456
  4. 2023QuIP: 2-Bit Quantization of Large Language Models With Guaranteescited by 406
  5. 2025TurboQuant: Online Vector Quantization with Near-optimal Distortion Ratecited by 119
  6. 2021EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learningcited by 53
  7. 2024QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overheadcited by 47
  8. 2025FP4 All the Way: Fully Quantized Training of LLMscited by 39
  9. 2025Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scalingcited by 33
  10. 2025NestQuant: Nested Lattice Quantization for Matrix Products and LLMscited by 19
  11. 2025Block Rotation is All You Need for MXFP4 Quantizationcited by 19
  12. 2025WUSH: Near-Optimal Adaptive Transforms for LLM Quantizationcited by 13
1 more
  1. 2024Optimal Quantization for Matrix Multiplicationcited by 12

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026High-Rate Quantized Matrix Multiplication IIcited by 4
  2. 2026Contraction-Gauge Preconditioning for Quantized Matrix Multiplicationcited by 2
  3. 2026A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Productscited by 1
  4. 2026Fast Matrix Multiplication in fp8: Certified Coefficient Optimization and Measured Errorcited by 1
  5. 2026Transforms for LLM Quantization: The Great Inversion and Format Co-Designcited by 0
  6. 2026A Note on Scaling in Randomly Rotated Quantization and Its Connection to the CDEF +1 Pythagorean Relationcited by 0
  7. 2026WaterKron and FlipFlop Hessian: Information-Theoretically Grounded Quantization with Kronecker-factored Hessianscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs
  2. 2026D2Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
  3. 2024Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
  4. 2024Low-Rank Correction for Quantized LLMs
  5. 2025Turning LLM Activations Quantization-Friendly
  6. 2026KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization
  7. 2024I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
  8. 2024What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation