Imagine a world where your AI assistant responds instantly, generating text with lightning speed. And now, imagine this happening without breaking the bank on expensive hardware. That's the promise of ConServe, a revolutionary system designed to squeeze every last drop of performance from your GPUs when serving Large Language Models (LLMs). LLMs, the brains behind applications like ChatGPT, are power-hungry beasts. Serving them efficiently requires juggling low latency for interactive tasks, like chatbots, with high throughput for background jobs, such as summarizing documents. Traditional systems often dedicate separate, over-provisioned GPU clusters to each task, leading to wasted resources and higher costs. ConServe flips the script by cleverly sharing GPUs between online and offline tasks. It's like having a smart traffic controller inside your server, dynamically allocating resources where they're needed most. When an urgent request, like a chatbot prompt, comes in, ConServe instantly preempts lower-priority tasks, ensuring a seamless user experience. It then uses a clever checkpointing mechanism to minimize wasted computation when resuming those background tasks. This dynamic resource allocation, combined with a scheduler that adapts to varying workloads and latency targets, allows ConServe to achieve a remarkable 2.35x higher throughput than standard systems. The result? Faster responses, more efficient processing, and significant cost savings. ConServe’s innovations open doors to a future where LLMs power more complex applications, handling a wider range of tasks concurrently. While challenges remain, like optimizing communication between parallel processes and handling ultra-long text sequences, ConServe represents a major step forward in making LLM serving more responsive, efficient, and affordable.