vLLM looks stronger for Qwen3.5 serving

This is the kind of infra question AI developers actually care about: not which stack is cooler, but which one gets tokens out faster without turning setup into a project of its own.

• –The strongest pro-vLLM argument in the thread is continuous batching, which matters more than raw single-request speed once a RAG pipeline starts issuing overlapping requests.
• –Community replies frame llama.cpp as the pragmatic fallback for single-user or constrained-memory deployments, especially when GGUF workflows and local tooling are already in place.
• –vLLM’s official docs back up the thread’s bias toward throughput with features like PagedAttention, continuous batching, and an OpenAI-compatible server.
• –llama.cpp still wins on portability and minimalism, with broad hardware support and lightweight local serving, which explains why it remains the default for many hobbyist and edge setups.
• –The real takeaway is that Qwen3.5 9B serving is becoming an infra-tuning problem, not just a model-selection problem; deployment ergonomics now directly shape RAG latency.