When to Choose SGLang Over vLLM: Multi-Turn Conversations and KV Cache Reuse

Deploying large language models on Runpod requires selecting an appropriate inference framework, with vLLM and SGLang offering distinct advantages based on use cases. vLLM is optimal for high-throughput batch inference where structured workflows and templated prompts benefit from its Automatic Prefix Caching, allowing for precise control and efficiency in predictable scenarios. Conversely, SGLang excels in dynamic, multi-turn conversations with its RadixAttention technique, which automatically optimizes caching for varied and evolving contexts, making it ideal for customer support chatbots or educational systems. Benchmark tests show SGLang provides a 10-20% performance improvement over vLLM in scenarios with complex, overlapping contexts, translating to significant cost savings, particularly in serverless environments. Users are encouraged to evaluate both frameworks using provided tools to determine the best fit for their specific production needs, as each framework offers unique strengths depending on the workload.