The VIDRAFT team from FINAL-Bench this week published VKUE (VIDRAFT Kernel Ubiquitous Engine) benchmark results on the Hugging Face community blog, running the same Ourbox-35B-JGOS weights across single-card B200 data center (aggregate 18,057 tok/s), single-card A10G (126 tok/s), 8GB-VRAM laptop (20 tok/s), all the way down to a completely GPU-less CPU server (~17 tok/s). Every number comes with a public reproduction path and live GPU/CPU demo — not just PPT promises. The key insight isn't a new kernel, but the physical nature of the model itself: Ourbox-35B-JGOS comes from the Qwen3.5-MoE / Qwen3-Next family, with 34.7B total parameters but only ~3B activated per token (256 experts top-8, Gated-DeltaNet linear attention interleaved with full attention). Decoding is memory-bandwidth bound: each token actually moves about 1.45 GB, 11× less than a same-sized dense 34B's 16.7 GB — that's the fundamental reason "the same weights can fit in an 8GB card". A strict A/B on the same 8GB laptop, the same VKUE engine, and the same Q3_K_M quantization: sparse A3B gets 20.01 tok/s, the Qwen2.5-32B dense baseline only 5.36 tok/s — the 3.7× speedup comes entirely from the sparsity. On capability, Ourbox-35B gets 86.4% (maj@8) / 70.7% (greedy) on GPQA Diamond. The team's slogan "VKAE chases speed, VKUE chases breadth" signals the engineering pivot — the dividends of sparse MoE shouldn't be monopolized by data centers, and self-hosting, edge deployment, and public-sector scenarios should all be able to use frontier-class reasoners.