arXiv AI
BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge
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BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks
arXiv:2608. 05926v1 Announce Type: cross Abstract: Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks.
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BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks
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 inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs.
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