arXiv:2608. 13057v1 Announce Type: cross Abstract: In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU.
By Jie Li, Chenxin Jia, Jinliang Shen, Cunzhuang Liu, Ruiyi Ding, Jianwen Xian, Kang He, Chengru Song
The paper investigates whether training Mixture-of-Experts (MoE) routers can improve memory‑bandwidth locality on consumer GPUs. Using a new zero‑surgery telemetry tool, the authors measure that a large Qwen3‑235B model is bottlenecked by disk‑based expert access, and that an LRU cache can serve a majority of requests. They pre‑register experiments training 137 M‑parameter MoE models with locality‑aware losses, finding that while cache misses can drop up to 60 % (99 % static‑pin hit rate), every configuration fails to meet a strict 1 % perplexity threshold, indicating a tight coupling between cache efficiency and model quality.
By Shriniwas Ramesh Suram
The paper investigates speculative decoding for mixture‑of‑experts (MoE) language models, focusing on how deduplication of expert reads can reduce byte traffic and improve decoding speed. By modeling step time as a function of bytes requested and kernel bandwidth, the authors derive conditions under which speculation outperforms plain decoding and identify the optimal draft length as a staircase function of acceptance rate. Experiments on llama.cpp Metal, MLX‑LM, and llama.cpp CUDA show that deduplication is only faster for very short draft lengths (5–8 tokens) or not at all, and the predicted crossover points closely match measured savings across multiple configurations.
By Yuzheng Lan
arXiv:2609.13922v1 Announce Type: new
Abstract: Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on...
By Matteo Fischetti
Prefetch‑Steered Routing (PSR) is a deterministic, cache‑aware method for selecting experts in Mixture‑of‑Experts models that limits physical weight copies to a logical cache of at most *n* experts per layer and token. On several large‑scale models (OLMoE‑1B‑7B, Moonlight‑16B‑A3B, Qwen3.6‑35B‑A3B) PSR achieves 2–2.3× faster decoding than exact routing with LRU and outperforms the Cache‑Prior baseline by 25–30% in many benchmarks, while maintaining comparable accuracy. The approach also shows that speculative block‑level expert selection can be faster than autoregressive decoding when GPU memory is constrained.
By Siming Huang
The paper investigates the performance impact of using a remote KV cache tier for long‑context language model serving. In a disaggregated deployment of GLM‑5.3 on 8xB300 nodes, enabling the remote tier initially increased the 16k‑token p99 latency from 3.96 s to 17.3 s, and synchronous loading caused severe queue instability. By sharding the KV store across context‑parallel ranks, partitioning the remote tier, adding asynchronous loading, and fixing client bugs, the authors reduced the p99 latency from 83.32 s to 0.67 s and increased prefill throughput by 14–34 %.
whyItMatters":"The study demonstrates that careful sharding and asynchronous handling of remote KV caches can dramatically improve latency and throughput in large‑scale LLM serving systems."
By Muhammad Ibrahim