VQV Signal
BALANCE proposes a hybrid approach combining Autoregressive decoding (AD) and Speculative decoding (SD) to improve LLM inference latency in wireless edge networks. AD generates tokens sequentially with high latency, while SD uses a smaller model to draft multiple tokens for faster verification.
BALANCE proposes a hybrid approach combining Autoregressive decoding (AD) and Speculative decoding (SD) to improve LLM inference latency in wireless edge networks. AD generates tokens sequentially with high latency, while SD uses a smaller model to draft multiple tokens for faster verification.
AI-assisted summary based on the listed source.
Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates...
This hybrid method aims to reduce inference latency in next-generation mobile networks, enabling more efficient LLM services at the wireless edge. Improving inference speed is critical for real-time applications relying on large language models in mobile environments.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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