Patent Claims: JiRack Dense Transformer Architecture (140B Extreme)

Inventor: Konstantin Vladimirovich Grabko
Entity: CMS Manhattan
Priority Date: December 21, 2025
Model Scale: 140 Billion Parameters (Extreme Dense / BF16)

I. FRONTIER SCALING CLAIMS

Claim 1: Extreme-Ratio Grouped-Query Attention (GQA)

A method for managing cross-GPU memory synchronization in models exceeding 100 billion parameters, characterized by a 12:1 Query-to-KV head ratio (96 Query heads to 8 Key/Value heads). This claim covers the specific optimization that allows the 140B model to maintain a large context window on standard 80GB HBM modules by drastically reducing the KV-cache growth rate.

Claim 2: Centum-Layer Depth Topology

A transformer architecture comprising exactly 100 sequential blocks, where the residual stream is balanced by dual-RMSNorm layers. This claim specifically protects the gradient flow stability achieved at this depth through the interplay of the 12,288-dimensional MODEL_DIM and the SwiGLU-Attention (SWA) fusion logic.

II. COMPUTATIONAL EFFICIENCY CLAIMS

Claim 3: High-Dimensional SWA Fusion Kernels

A method for executing a 140B scale transformer where the SwiGLU hidden dimension is fixed at 34,816. This claim protects the specific ratio of FFN-to-Model dimensionality (approx. 2.83x) which, when fused with the Attention projection in a single compute kernel, optimizes the matrix core utilization on AMD CDNA3 and NVIDIA Blackwell architectures.

Claim 4: HBM3/4 Predictive Pre-fetching (BRE)

A memory routing system (BRE) specifically calibrated for 140B weights, utilizing predictive token heuristics to saturate the 12,288-width embedding channel. This ensures that the memory controller pre-loads the next required weight blocks into the GPU's L3/Shared memory cache before the forward pass of the 100-layer stack begins.

III. IP PROTECTION & PROVENANCE CLAIMS

Claim 5: Immutable Scale-Invariant Authorship Proof

A non-computational authorship verification buffer (proof_of_authorship) containing the ASCII-encoded signature of Konstantin Vladimirovich Grabko. This claim ensures that even if the 140B model is pruned, distilled into a 7B model, or converted to 4-bit quantization (GGUF/AWQ), the original IP owner can be cryptographically verified.

Claim 6: Shared-Weight Vocabulary Projection

A method for reducing the physical footprint of a 140B model by tying the weights of the input embedding and the output head. This results in an immediate reduction of approximately 617 million parameters, allowing the 140B model to fit into one additional GPU node that would otherwise be required for a non-tied architecture.

IV. CONTEXTUAL PERFORMANCE CLAIMS

Claim 7: Adaptive Sliding Window for Extreme Models

An attention window management method with a fixed WINDOW_SIZE of 2,048 tokens. This claim covers the logic where the KV-cache is truncated at every layer of the 100-layer stack, preventing the memory-over-computation bottleneck that typically occurs in dense models of this scale.

Legal Enforcement Notice
Any entity or individual utilizing the 12:1 GQA logic, 100-layer SWA topology, or BRE routing for commercial deployment must adhere to the Commercial License Agreement V.1.2:

Payment of the 5% Net Revenue Royalty to Konstantin Vladimirovich Grabko.
Mandatory display of the attribution: "Powered by CMS Manhattan JiRack Technology."
Absolute prohibition on filing patent claims that overlap with the architectures disclosed in these claims.