492 citations · 1.5k across the 65 of their papers we have counts for
19 papers · 1 filter
Specifications: The missing link to making the development of LLM systems an engineering discipline
Ion Stoica, Matei Zaharia, Joseph Gonzalez +8
Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This ca…
HashAttention: Semantic Sparsity for Faster Inference
Aditya Desai, Shuo Yang, Alejandro Cuadron +3
Leveraging long contexts is crucial for advanced AI systems, but attention computation poses a scalability challenge. While scaled dot-product attention (SDPA) exhibits token spars…
GameArena: Evaluating LLM Reasoning through Live Computer Games
Lanxiang Hu, Qiyu Li, Anze Xie +4
Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination a…
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…
Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
Alexander Krentsel, Peter Schafhalter, Joseph E. Gonzalez +3
Prevailing wisdom asserts that one cannot rely on the cloud for critical real-time control systems like self-driving cars. We argue that we can, and must. Following the trends of i…
Efficient LLM Scheduling by Learning to Rank
Yichao Fu, Siqi Zhu, Runlong Su +3
In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple Fir…
Post-Training Sparse Attention with Double Sparsity
Shuo Yang, Ying Sheng, Joseph E. Gonzalez +2
The inference process for large language models is slow and memory-intensive, with one of the most critical bottlenecks being excessive Key-Value (KV) cache accesses. This paper in…
MPC-Minimized Secure LLM Inference
Deevashwer Rathee, Dacheng Li, Ion Stoica +2
Many inference services based on large language models (LLMs) pose a privacy concern, either revealing user prompts to the service or the proprietary weights to the user. Secure in…
RouteLLM: Learning to Route LLMs with Preference Data
Isaac Ong, Amjad Almahairi, Vincent Wu +5
Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and c…
TurboSpec: Closed-loop Speculation Control System for Optimizing LLM Serving Goodput
Xiaoxuan Liu, Jongseok Park, Langxiang Hu +10
Large Language Model (LLM) serving systems batch concurrent user requests to achieve efficient serving. However, in real-world deployments, such inter-request parallelism from batc…
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick +5
The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks.…
Crafting Interpretable Embeddings by Asking LLMs Questions
Vinamra Benara, Chandan Singh, John X. Morris +4
Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks. However, their opaqueness and proliferation into scient…
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Justin Cui, Wei-Lin Chiang, Ion Stoica +1
Large Language Models (LLMs) require careful safety alignment to prevent malicious outputs. While significant research focuses on mitigating harmful content generation, the enhance…
Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity
Tyler Griggs, Xiaoxuan Liu, Jiaxiang Yu +4
Large language models (LLMs) are increasingly integrated into many online services, yet they remain cost-prohibitive to deploy due to the requirement of expensive GPU instances. Pr…
MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving
Jiangfei Duan, Runyu Lu, Haojie Duanmu +5
Large language models (LLMs) have demonstrated remarkable performance, and organizations are racing to serve LLMs of varying sizes as endpoints for use-cases like chat, programming…
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng +8
Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To addres…
RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain +4
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additi…
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
Yichao Fu, Peter Bailis, Ion Stoica +1
Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded, resulting in high latency and significant wastes of the parallel processing power of modern acc…
Fairness in Serving Large Language Models
Ying Sheng, Shiyi Cao, Dacheng Li +5
High-demand LLM inference services (e.g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading. To ensure that all client reque…