Autoregressive decoding remains a key bottleneck for LLM inference, due to its token-by-token nature. Speculative decoding is proposed to reduce latency by drafting multiple tokens and verifying them with fewer target-model passes. However, existing methods either incur auxiliary-drafter overhead or rely on brittle lexical span reuse. In structured tasks (e.g., code generation and text editing), we observe significant locality of reference and semantic redundancy for potential semantic context reuse. In light of this, we present Semantic Retrieval Speculative Decoding (SRSD), a training-free scheme that treats intermediate-layer hidden states as an in-context semantic index. At each decoding step, SRSD retrieves semantically similar past positions. Multiple candidates are merged into a compact tree and verified jointly in a single forward pass. SRSD requires no auxiliary models or external datastores. Across a variety of popular LLMs, SRSD achieves up to 5.42× throughput speedups over state-of-the-art training-free methods and parametric drafters. The code is available at https://github.com/Secbrain/SRSD.

Large language models are reshaping internet services. Serving these models is often costly, as it requires multiple high-end GPUs. Consumer-grade GPUs offer cheaper computational power, providing an opportunity for more cost-efficient LLM serving.Prior efforts have explored distributed serving at scale, primarily focusing on model deployment strategies. However, communication efficiency has emerged as a challenge due to the imbalance in data transfer volumes between the two phases of inference: prefill and decode. Prefill requests can involve transmitting up to 1000 times more data than decode requests, leading to decode requests being delayed. Consequently, servers are underutilized while waiting for decode requests. In this paper, we present MoLink, an efficient distributed LLM serving system. It splits the prolonged transmission volume of prefill requests into smaller chunks and carefully scheduling their transmission. It consists of two parts: (i) a transmission scheduling algorithm that fairly determines whether to transmit prefill or decode requests, and (ii) a chunking determination algorithm that determines the transmit volume for prefill requests just-in-time. Our evaluation demonstrates that MoLink reduces TTFT, TPOT, and latency compared to the state-of-the-art distributed LLM serving system, with a maximum reduction of up to 46%.