arXiv Machine Learning

SoftWater: Class-Aware Rate Allocation for Softmax Quantization

arXiv:2608. 12026v1 Announce Type: new Abstract: Post-training quantization pipelines routinely leave the softmax output layer in high precision.

arXiv Machine Learning

Jul 7

Variable Bit-width Quantization: Learning Per-Group Precision for "Bigger-but-Smaller" Language Models

arXiv:2607. 02893v1 Announce Type: new Abstract: Low-bit quantization shrinks language models but treats precision as a single global hyper-parameter: every weight uses the same bit-width.

By Hamish Ogilvy

llms efficiency

arXiv Machine Learning

Jul 14

RDQ: Residual Distribution Quantization for Large Language Models

arXiv:2607. 10137v1 Announce Type: new Abstract: Post-training quantization (PTQ) of large language models degrades sharply below 4-bit precision.

By Prateek Singh

llms efficiency benchmarks

arXiv Machine Learning

Jul 15

Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate

arXiv:2607. 12266v1 Announce Type: new Abstract: Mixed-precision quantization must decide which parts of a model to keep at higher precision.

By Joshua Hill

efficiency

arXiv AI

Jun 4

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606. 04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degradation.

By Devleena Das, Rajeev Patwari, Elliott Delaye, Ashish Sirasao

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 31

Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents

arXiv:2607. 27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless.

By Jiwon Jang, Kisu Yang, Heuiseok Lim, Hyunwoo Park

llms agents efficiency benchmarks safety

arXiv AI

Jun 9

High-Rate Quantized Matrix Multiplication II

arXiv:2605. 13768v2 Announce Type: replace-cross Abstract: This is the second part of the work investigating quantized matrix multiplication (MatMul).

By Or Ordentlich, Yury Polyanskiy

llms efficiency

arXiv Machine Learning

Jun 29

RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory

arXiv:2605. 06675v2 Announce Type: replace Abstract: Large language models cache all previously computed key-value (KV) pairs during generation, and this KV cache grows linearly with sequence length, making it a primary memory bottleneck for serving.

By Fei Zuo, Zikang Zhou, Hao Cong, Xiaoyan Xi, Ho Fai Leung

llms efficiency

arXiv AI

Jul 13

Signed Symmetric Quantization for Few-Bit Integers

arXiv:2607. 08779v1 Announce Type: cross Abstract: The signed integer alphabet contains one more negative representable value than positive.

By Ian Colbert, Eashan Dash, Pablo Monteagudo-Lago, Juan Amboage, Srinidhi N, Giuseppe Franco, Nicholas J. Fraser, Arun Ramachandran

llms efficiency

arXiv AI

Jul 7

HiFA4: Training-Free 4-bit FlashAttention on Ascend HIF4 NPUs for LLM Inference

arXiv:2607. 04302v1 Announce Type: cross Abstract: We present HiFA4, a post-training operator-level design that executes both QK^T and PV in FlashAttention as 4-bit HIF4 Cube GEMMs for LLM inference on Ascend NPUs, while maintaining the online softmax state in FP16.

By Hui Dong, Yanzhao Li, Jie Gao, Chunlu Li, Zhiyuan Zhang, Yupeng Sun, Zhenyuan Chen, Zhiqiang Zou

llms nlp efficiency benchmarks

arXiv Machine Learning

Jun 11

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs

arXiv:2606. 12280v1 Announce Type: new Abstract: Post-training quantization lets large text-to-image diffusion transformers run on consumer GPUs, yet the hardware-specific trade-offs are seldom measured directly.

By Deep Gandhi, Ali Asaria, Tony Salomone

llms diffusion computer-vision efficiency benchmarks

arXiv Machine Learning

Jun 2

WUSH: Near-Optimal Adaptive Transforms for LLM Quantization

arXiv:2512. 00956v3 Announce Type: replace Abstract: Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization errors.

By Jiale Chen, Vage Egiazarian, Roberto L. Castro, Torsten Hoefler, Dan Alistarh

llms efficiency

arXiv Machine Learning

Aug 11

RotaryQuant: Fitting 120B MoE Models on Consumer Hardware via Fused Compressed-Space Attention

arXiv:2608. 08081v1 Announce Type: cross Abstract: Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident weight matrices, key-value (KV) cache state that grows linearly with context, and dozens of expert sublayers that must be paged on demand.

By Anthony. Lui, Mohamed. Elsaied, N. P. Savani

llms efficiency

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