The paper introduces Colla-Q, a Mixture-of-Experts (MoE) quantization technique that uses activation entropy to allocate bit-widths across experts. By balancing performance among experts, Colla-Q improves overall MoE accuracy and reduces reliance on calibration datasets. The method aims to maintain robustness and stability in quantized MoE models.
arXiv:2606. 04980v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures scale model capacity through sparse expert activation, but their deployment remains memory-bound because all expert weights must reside in memory.
arXiv:2606. 00079v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation, but their deployment remains memory-intensive because all expert weights must be kept resident in memory.
arXiv:2607. 23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget.
GAMMA is a post‑training framework that learns module‑wise precision preferences for mixed‑precision quantization of large language models. It optimizes a teacher‑forced hidden‑state reconstruction objective under an augmented Lagrangian constraint and then projects the learned preferences into exact budget‑feasible discrete assignments via integer programming. Because the learned preferences encode a stable sensitivity ranking, a single training run can be reused for any deployment budget, reducing per‑budget adaptation from hours to minutes and outperforming fixed‑precision baselines and search‑based methods on Llama and Qwen models.
arXiv:2605. 26660v2 Announce Type: replace Abstract: Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in the ultra-low-bit regime remains challenging.
The paper introduces FAMPWQ, a Fisher information-based Adaptive Mixed Precision Weight Quantization method designed to improve Large Language Model inference on commodity GPUs. It uses a Fisher information metric to assess layer-wise sensitivity and a reinforcement learning-based bit-width allocator to adaptively assign precision per layer. Experiments across seven models and five benchmarks show significant gains in perplexity, accuracy, and LLM-as-a-judge performance compared to seven baseline approaches.
The paper introduces a curiosity‑driven quantized Mixture‑of‑Experts framework that routes inputs based on Bayesian epistemic uncertainty across heterogeneous experts (BitNet ternary, 1‑16 bit BitLinear, post‑training quantization). On audio classification benchmarks, 4‑bit quantization preserves 99.9 % of full‑precision F1 while achieving 4× compression and 31 % energy savings, and curiosity‑driven routing further improves accuracy and reduces cross‑fold variance by up to 85 %. The routing is self‑organizing, allocating the most uncertain samples to the high‑precision expert, and the method demonstrates statistical parity with full precision across datasets.
arXiv:2505. 22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible.
arXiv:2410. 13056v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes.
arXiv:2606. 04115v1 Announce Type: cross Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all layers is sub-optimal in terms of both performance and accuracy.
arXiv:2608.30564v1 Announce Type: cross Abstract: Mixed-precision quantization (MPQ) assigns a different bitwidth to each linear layer of a large language model (LLM) to minimize the quantization-ind...
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.