arXiv Artificial Intelligence

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

1 hour ago 07.08.2026 07:00 👁 2 views
BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

Quick summary

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. 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. Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneo

Key takeaways

  • 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.
  • 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.
  • Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneo

Why it matters

“BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗