The paper introduces GraphTok, a GPU-based tokenizer that executes the entire tokenization pipeline as a single CUDA graph, matching reference token IDs for models such as GPT‑2, Llama 3, Qwen 2.5, DeepSeek‑V3, and Gemma 3. GraphTok achieves significant speedups, encoding one million GPT‑2 tokens in 1.7–3.2 ms and outperforming the leading CPU tokenizer gigatoken by 4.7–6.0× on Llama 3. In a vLLM deployment with cached prefixes, it reduces mean time‑to‑first‑token by 2.1–2.4× and dramatically increases request throughput, sustaining 518 req/s of 121k‑token traffic versus 296 with gigatoken.
By Mohammad Siavashi, Pascal Schwinte, Ali Banaei, Gerald Q. Maguire Jr., Marco Chiesa, Dejan Kosti\'c
Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation. Their bidirectional attention prevents exact autoregressive-style KV caching, since committing one position shifts the KV activations of all others.
The paper investigates how speculative decoding—where a draft model proposes multiple future tokens that are then verified by the target model—affects energy consumption in large language model serving under latency constraints. It finds that energy savings depend heavily on the acceptance rate of drafted tokens, which varies by model architecture, drafting scheme, and the type of text (e.g., conversation vs. code, English vs. Romansh). The study shows that while speculation can reduce energy per token at small batch sizes, at larger batches the verification cost dominates, potentially turning speculation into an energy loss, especially when acceptance rates are low.
By Bole Ma, Ayesha Afzal, Jan Eitzinger, Harald Koestler, Gerhard Wellein
arXiv:2609.37626v1 Announce Type: cross
Abstract: No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independe...
By Chuan Liu, Shuoming Zhang, Zhicheng Li, Qianqi Sun, Ruiyuan Xu, Qiuchu Yu, Xiyu Shi, Huimin Cui, Jiacheng Zhao
arXiv:2607. 04206v1 Announce Type: cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocation.
By Nitin Kedia, Saurabh Agarwal, Myungjin Lee, Aditya Akella
The paper investigates how prefix caching, a default optimization in open‑source LLM serving stacks, affects reproducibility when combined with weight quantization. Experiments on an eighty‑episode multi‑turn agentic tool‑use workload show that enabling the cache causes the agent’s trajectory to change in 36.2 % of episodes at 16‑bit precision and 75.0 % at 4‑bit precision, while disabling the cache yields perfectly reproducible runs. The study identifies specific cache‑related settings that drive run‑to‑run divergence and demonstrates that cached serving is deterministic only when the cache state is preserved, which is not the case in typical deployments.
By Aditi Patodiya