[lcus_masonry_article]<\/p>\n
The AutoPhi V18 Voxel Processor \u2014 Mini Series #200<\/strong> (SKU: APV18-MIN-0200-65NM) is the final and most powerful processor in the Mini tier. Priced at $76,440.00<\/strong>, it delivers 242.95 MFLOPS<\/strong> on the 65nm node with approximately 84 qubits<\/strong>. It represents the ceiling of the Mini Series and the gateway to the Entry tier’s Giga-class performance.<\/p>\n List Price:<\/strong> $76,440.00 USD<\/p>\n At $76,440, the Mini #200 costs 76.4x more than the Seed #1 but delivers over 2,429x more performance. The cost-per-MFLOP is approximately $315<\/strong>, and the cost-per-KFLOP has collapsed to roughly $0.31<\/strong> \u2014 a 97% improvement over the Seed baseline. This exponential cost-efficiency curve is the hallmark of the AutoPhi V18 catalog: each tier delivers disproportionately more compute per dollar.<\/p>\n At 84 qubits, the quantum subsystem is substantial. It can represent 2^84 \u2248 1.93 \u00d7 10^25 quantum states simultaneously \u2014 a computational space that no classical computer can simulate in reasonable time. For quantum algorithm research, drug discovery simulations, and materials science modeling, the Mini #200 provides quantum resources that were impossible at lower tiers.<\/p>\n Compute Performance:<\/strong> 242.95 MFLOPS<\/p>\n Fabrication Node:<\/strong> 65nm<\/p>\n Quantum Integration:<\/strong> ~84 qubits<\/p>\n Performance Class:<\/strong> Mega-class (approaching Giga-class)<\/p>\n At nearly 243 MFLOPS, the Mini #200 is the most powerful Mega-class processor in the catalog. The 65nm node is fully optimized, with voxel configurations that extract maximum performance from every transistor. The 84-qubit quantum layer enables quantum error correction codes, deep variational circuits, and quantum machine learning models that require significant qubit depth. This processor is the natural choice for research institutions, national laboratories, and enterprise quantum computing programs that need production-grade hybrid compute.<\/p>\nCost Breakdown<\/h2>\n
Performance Specifications<\/h2>\n