arXiv:2605.15572v2 Announce Type: replace Abstract: The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and massive activations on pre-2024 LLaMA-style models, and the downstream activation-quantization stack inherits that picture without revisiting it for the post-LLaMA open-model boom. We ask the deployment-oriented question: how large can activations get in modern open LLMs, and how does this magnitude vary across families, generations, and training stages? Under a unif
This paper re-evaluates fundamental constraints in LLM deployment post-2024, addressing the massive growth and diversity in open models compared to previous LLaMA-style research.
Understanding LLM activation dynamics is critical for efficient quantization, stable inference, and the development of future AI hardware, impacting cost and capability.
The existing understanding of LLM activation behavior derived from older models is being updated, influencing how researchers and engineers approach LLM optimization and hardware design.
- · AI hardware manufacturers
- · LLM deployment platforms
- · Quantization specialists
- · Inefficient LLMs
- · Cloud providers with suboptimal inference
Improved and more stable low-bit quantization techniques for large language models.
Reduced computational costs and increased accessibility for deploying powerful LLMs on various hardware.
Accelerated development of specialized AI chips and architectures tailored for efficient LLM inference, potentially decentralizing AI compute power.
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