5 papers · 1 filter
LatentBox: Storing AI-Generated Images at Scale via a Latent-First Design
Zirui Wang, Yunjia Zheng, Tingfeng Lan +4
The explosive growth of AI-generated images has created a sustainability challenge for storage infrastructure. Platforms like Midjourney and Adobe Firefly already host billions of…
TStore: Rethinking AI Model Hub with Tensor-Centric Compression
Tingfeng Lan, Zirui Wang, Yunjia Zheng +3
Modern AI models are growing rapidly in size and redundancy, leading to significant storage and distribution challenges in model hubs. We present TStore, a tensor-centric system fo…
λScale: Enabling Fast Scaling for Serverless Large Language Model Inference
Minchen Yu, Rui Yang, Chaobo Jia +9
Serverless computing has emerged as a compelling solution for cloud-based model inference. However, as modern large language models (LLMs) continue to grow in size, existing server…
MorphServe: Efficient and Workload-Aware LLM Serving via Runtime Quantized Layer Swapping and KV Cache Resizing
Zhaoyuan Su, Zeyu Zhang, Tingfeng Lan +4
Efficiently serving large language models (LLMs) under dynamic and bursty workloads remains a key challenge for real-world deployment. Existing serving frameworks and static model…
ZenFlow: Enabling Stall-Free Offloading Training via Asynchronous Updates
Tingfeng Lan, Yusen Wu, Bin Ma +7
Fine-tuning large language models (LLMs) often exceeds GPU memory limits, prompting systems to offload model states to CPU memory. However, existing offloaded training frameworks l…