Qwen3.8 27B

User compares six engines running Qwen3.8 27B on a single RTX 5090 32GB, reporting a plain-mode three-workload decode average of 80.0 t/s for TensorSharp rising to 234.4 t/s with DFlash2 speculation. Setup is TensorSharp's direct-CUDA backend with an NVFP4 GGUF target and Q4_K_M drafter, 40,960-token context, thinking disabled and vision enabled; vLLM and SGLang used NVFP4/FP8 safetensors, NInfer and velox a mixed NVFP4/FP8 .ninfer target. DFlash2 raises summarized single-request decode roughly 2.5-3.6x across engines; NInfer leads speculative decode at 280.5 t/s average and four-request throughput at 488 t/s, while llama.cpp has the lowest short-prompt TTFT at 27 ms. The author notes up to about 4% decode variation between runs and that quantized targets and cache formats differ between engines.