24 citations · 43 across the 31 of their papers we have counts for
12 papers · 1 filter
LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
Yi Pan, Haocheng Xi, Kan Zhu +10
Recent LLMs increasingly adopt hybrid designs that replace standard attention with linear attention, such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA). Although they comp…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Kan Zhu, Mathew Jacob, Chenxi Ma +4
Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging. Progress on this challenge requires understanding real wor…
M*: A Modular, Extensible, Serving System for Multimodal Models
Atindra Jha, Naomi Sagan, Keisuke Kamahori +9
We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, ac…
MURMUR: An Efficient Inference System for Long-Form ASR
Wei-Tzu Lee, Keisuke Kamahori, Baris Kasikci
Long-form automatic speech recognition (ASR) requires both high accuracy and low latency, but existing systems force a trade-off between the two. Chunk-based pipelines process audi…
VoxServe: Streaming-Centric Serving System for Speech Language Models
Keisuke Kamahori, Wei-Tzu Lee, Atindra Jha +4
Deploying modern Speech Language Models (SpeechLMs) in streaming settings requires systems that provide low latency, high throughput, and strong guarantees of streamability. Existi…
Accelerating Large-Scale Reasoning Model Inference with Sparse Self-Speculative Decoding
Yilong Zhao, Jiaming Tang, Kan Zhu +10
Reasoning language models have demonstrated remarkable capabilities on challenging tasks by generating elaborate chain-of-thought (CoT) solutions. However, such lengthy generation…
Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
Kan Zhu, Tian Tang, Qinyu Xu +7
Long-context models are essential for many applications but face inefficiencies in loading large KV caches during decoding. Prior methods enforce fixed token budgets for sparse att…
LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima +1
Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient d…
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models
Yile Gu, Yifan Xiong, Jonathan Mace +4
Observability in cloud infrastructure is critical for service providers, driving the widespread adoption of anomaly detection systems for monitoring metrics. However, existing syst…
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
Yilong Zhao, Shuo Yang, Kan Zhu +5
Offline batch inference, which leverages the flexibility of request batching to achieve higher throughput and lower costs, is becoming more popular for latency-insensitive applicat…
Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models
Keisuke Kamahori, Tian Tang, Yile Gu +2
Large Language Models (LLMs) with the Mixture-of-Experts (MoE) architectures have shown promising performance on various tasks. However, due to the huge model sizes, running them i…
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
Yilong Zhao, Chien-Yu Lin, Kan Zhu +7
The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM s…