Qwen3.8-Flash-Next Benchmark: SGLang Beats llama.cpp by 7.3x in Time-to-First-Token

A benchmark testing Qwen3.8-Flash-Next at a 262K context window revealed massive performance gaps across LLM serving engines on an NVIDIA RTX PRO 6000 Blackwell GPU. SGLang achieved a time-to-first-token (TTFT) of 35.4s compared to 258.4s on baseline llama.cpp, demonstrating a 7.3x reduction in initial waiting time. Long-context LLM applications depend heavily on low prefill latency, making engine selection and engine-specific optimizations crucial for practical usability. While server-oriented frameworks like SGLang excel at processing large context windows, lightweight engines like llama.cpp still maintain a major advantage in quick cold startup times. SGLang led decode throughput at full context with 126.9 tok/s versus 20.3 tok/s for baseline llama.cpp, though llama.cpp with a Multi-Token Prediction (MTP) draft head improved its decode speed up to 1.69x. Conversely, startup times favored llama.cpp, which began responding in 16 seconds compared to 108 seconds for SGLang and 126 seconds for FreeToken.