BitLLM: Efficient Low-Bit, Conversion-Hiding LLM Decoding on Mobile GPUs

The rapid scaling of large language models (LLMs) has unlocked remarkable capabilities across various domains. However, deploying LLMs on mobile devices remains challenging because of their high computational and memory requirements. Low-bit quantization (e.g., 4-bit weights, 8-bit activations) can reduce model size while preserving accuracy, but its practical efficacy on mobile GPUs has been limited so far. In this paper, we identify that the performance gap stems primarily from frequent data …

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-03. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern