VLLM or llama.cpp: Choosing the right LLM inference engine for your use case (developers.redhat.com)

A recent benchmark analysis has pitted two leading LLM inference engines, vLLM and llama.cpp, against each other, revealing their distinct strengths and appropriate use cases. vLLM is engineered for high-throughput, multi-user scenarios, showcasing remarkable scalability on NVIDIA H200 GPUs, where it delivers significantly higher request throughput and responsiveness under concurrent loads. In contrast, llama.cpp is tailored for single-stream efficiency and portability, making it ideal for low-concurrency tasks and deployment on consumer-grade hardware, thanks to its lightweight architecture and fast startup times. The testing demonstrated that while vLLM excels in multi-user environments, optimizing request handling and maintaining low Time to First Token (TTFT) even at high concurrency, llama.cpp maintains a consistent output performance but struggles with increasing request loads due to its queuing model. This analysis emphasizes the importance of selecting the right tool based on deployment needs: vLLM is the standout for scalable, performance-critical applications in enterprise settings, whereas llama.cpp remains a solid choice for developers prioritizing efficiency and portability in smaller or offline setups.