Update article with read more

content/posts/preble.md

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 LLM prompts are growing more complex and longer with [agents](https://arxiv.org/abs/2308.11432), [tool use](https://platform.openai.com/docs/guides/function-calling), [large documents](https://arxiv.org/html/2404.07143v1), [video clips](https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/#context-window), and detailed [few-shot examples](https://arxiv.org/pdf/2210.03629). These prompts often have content that is shared across many requests. The computed intermediate state (KV cache) from one prompt can be reused by another for their shared parts to improve request handling performance and save GPU computation resources. However, current distributed LLM serving systems treat each request as independent and miss the opportunity to reuse the computed intermediate state. 
 
 We introduce [**Preble**](https://github.com/WukLab/preble), the first distributed LLM serving system that targets long and shared prompts. Preble achieves a **1.5-14.5x** average and **2-10x** p99 latency reduction over SOTA serving systems. The core of Preble is a new E2 Scheduling that optimizes load distribution and KV cache reutilization.  Preble is compatible with multiple serving backends such as [vLLM](https://github.com/vllm-project/vllm) and [SGLang](https://github.com/sgl-project/sglang).
+<br/><br/>
+[Read More...](https://mlsys.wuklab.io/posts/preble/)
 "
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 Author: Vikranth Srivatsa and Yiying Zhang

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