arXiv AI
SSDi8: Accurate and Efficient 8-bit Quantization for State Space Duality
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OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond
arXiv:2605.19660v3 Announce Type: replace Abstract: The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a domina...
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Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.
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LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers
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Beyond Dense Adam States: Adaptive Log-Space Quantization for Memory-Efficient Optimizers
The paper introduces Adaptive Log‑Space (AL) quantization, a block‑wise representation that adapts the non‑zero range per block and preserves the exact‑zero invariant for non‑negative optimizer states. AL8 and AL16 are combined with signed‑momentum encodings and state‑specific precision choices, rather than a single policy for all states. Experiments on TinyLlama‑1.1B and GPT‑2 show that AL‑based quantization can match or exceed full‑precision performance while dramatically reducing optimizer‑state storage.
arXiv AI
Jun 6
Channel-Wise Mixed-Precision Quantization for Large Language Models
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Hugging Face Trending Papers
Jun 11
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.