Pyrite Phase 3: Dense-Stream --- In-Flight Lossless Decompression and Hybrid Tensor Decomposition for Dense Foundation Models on Consumer Hardware

While Mixture-of-Experts (MoE) architectures achieve efficient local inference through spatial routing sparsity (e.g., Pyrite Phase 2), dense foundation models unconditionally activate 100% of their parameters at every token step. Consequently, executing dense 30B--70B parameter models on consumer hardware with 16 GB unified memory has historically suffered from extreme I/O transfer bottlenecks (~14 seconds/token in naive layer offloading). We introduce Pyrite Phase 3 (Dense-Stream), a unified mathematical and systems architecture that shatters the dense I/O bottleneck through three orthogonal innovations: (1) In-Flight Lossless Tensor Codec: Exploiting IEEE 754 floating-point exponent clustering via Byte-Shuffle bit-plane transposition, we isolate low-entropy magnitude planes from high-entropy mantissas. Combined with Zstandard entropy coding, Dense-Stream achieves a 31.5% reduction in physical SSD transfer size with exact zero error (epsilon = 0.0), effectively elevating Apple Silicon NVMe bandwidth from 4.5 GB/s to an effective 6.57 GB/s. (2) Ping-Pong Double Buffering: A pipelined asynchronous prefetcher overlaps CPU decompression and SSD DMA transfers with Apple Metal GPU execution, hiding layer I/O latency completely behind GPU computation. (3) Hybrid Orthogonal Decomposition (SVD + Lossless Residual): Decoupling dense matrices into a permanent RAM-resident orthogonal skeleton (r=64, retaining >54% cosine similarity in 6.25% of parameter volume) and an ultra-low entropy residual matrix streamed on-demand. Empirical benchmarks on real-world unquantized BF16 transformer layers demonstrate bit-exact numerical reconstruction, seamless I/O latency hiding, and stable dense execution within 1.6 GB to 3.2 GB of physical RAM on a 16 GB Apple M2 Pro Mac.