The core bottleneck of large-model deployment cost has never been parameter count, but how many concurrent requests a single node can hold — and this is especially true for MoE models, where active parameters, KV cache and Mamba state together cap the upper limit. NVIDIA's Nemotron team released Nemotron-Labs-3-Puzzle-75B-A9B (arXiv: 2607.04371), compressing the 120.7B / 12.8B-active Nemotron-3-Super down to 75.3B / 9.3B-active, but with non-uniform pruning. The core method is called Iterative Puzzle: it feeds the MoE intermediate channels, the number of active experts, and the Mamba SSM state into a mixed-integer-programming solver, choosing the optimal per-layer implementation backwards from the deployment SLA. Three compression stages each have 24B/43.2B/52.8B tokens of KD recovery, with the long-context stage then expanded to 128K–512K fine-tuning. After NVFP4 quantization, 8×B200 service throughput is +2.03× (8K/64K decoding), and single-card H100 1M-context concurrency goes from 1 to 8 — weights shrink from 70GB to 44.5GB. The cost is Arena-Hard-V2 -4.2, SWE-Bench -2.6 (instruction-following and agent-class losses the largest); long-context RULER 1M drops only 1.7, almost lossless. This deployment-for-performance workflow will most likely become the standard for the next generation of open-source LLMs — putting expert intermediate dimensions, top-k, Mamba state, and attention layers all into the same NAS solver is exactly the engineering discipline the community is still missing. Combined with NVFP4 and multi-token prediction heads, 4090 and H100 can both deliver flagship throughput, directly pulling down the compute threshold for open-source large-model landing.