Large language models (LLMs) are revolutionizing how we interact with technology, but their computational demands present significant challenges. Serving multiple users efficiently and fairly, ensuring everyone gets a reasonable response time, is a complex balancing act. Imagine a busy restaurant—you want to serve everyone promptly, not just prioritize a few while others wait indefinitely. This is where FastSwitch comes in. This innovative serving system optimizes how LLMs switch between different user requests (context switching), minimizing the overhead that can lead to slowdowns. Traditional LLM serving systems often prioritize throughput, or the number of requests processed, but this can be unfair to some users. Preemption-based scheduling attempts to address fairness by dynamically adjusting request priorities. However, this frequent switching between tasks introduces significant overhead as the LLM needs to load and unload the relevant information (KV cache) from memory, much like a chef constantly switching recipes and ingredients. FastSwitch tackles this by implementing three key improvements. First, it uses a “Dynamic Block Group Manager” to organize the memory more efficiently, reducing wasted bandwidth and speeding up data transfer. Think of this as organizing the kitchen for maximum efficiency. Second, the “Multithreading Swap Manager” allows the LLM to handle multiple swapping operations concurrently, preventing the system from idling while waiting for data transfers. It’s like having multiple chefs working in parallel. Finally, a “KV Cache Reuse Mechanism” intelligently reuses previously stored information in multi-turn conversations, minimizing redundant data transfers. This is similar to a chef remembering frequently used ingredients. These improvements significantly reduce latency – the time it takes for a user to receive a response – and increase overall throughput. Experiments with models like LLaMA-8B and Qwen-32B show FastSwitch achieves remarkable speedups in key performance metrics, particularly under high-stress conditions with frequent priority updates. This means faster, more responsive LLMs that can handle many users simultaneously. FastSwitch represents a significant step towards making LLM serving more efficient and equitable, paving the way for broader adoption and a better user experience in a world increasingly reliant on these powerful AI models.