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Decompose vLLM's enforce_eager decode step (attention / weight GEMM / sampling / host loop) on GB10 (DGX Spark, sm_121) and attribute the measured ~2.4x NVFP4 decode-throughput gap to its parts, from source reading plus the existing nsys decode trace and H2H bench logs. Key finding: the gap is dominantly a KERNEL-efficiency gap (~80-90%), not a host-overhead gap. llama's GPU is already ~94.6% busy during steady decode, so a CUDA-graphed decode is a minority lever (~10-20% of the gap, bounded by the GPU-idle bubble), not the silver bullet. vLLM's wins: in-kernel paged-decode read (no gather tax), faster long-context attention, fused native-FP4 / grouped-Marlin GEMM, and O(1)-in-ctx GDN linear-attention layers on these Qwen3.6 hybrids. vLLM achieved 2.4x with synchronous scheduling and no CUDA graphs. Evidence: vllm 0.23.0 source (gpu_model_runner, flash_attn/gdn backends, modelopt/marlin GEMM, v1/sample), reproduced nsys kernel categorization (cat2.py), and QWEN36_NVFP4_BENCH / DECODE_GAP_STUDY / CONTINUOUS_BATCH_SCHEDULER_SCOPE. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io>