ZAQERIS AI — Independent Foundation Architecture Explorer & Empirical Dossier

Architectural Positioning: Lightweight Foundational Family vs Heavyweight Large Model Tier

ZAQERIS AI engineers two complementary model families: AXIOM serves as the agile, high-throughput foundation tailored for single-workstation edge execution, while ZAQION represents the absolute heavy-weight cognitive Large Model engineered for multi-step mathematical deduction and complex systems software synthesis.

Architectural Dimension	AXIOM Family Lightweight High-Throughput Core	ZAQION Family Heavyweight Large Model Tier
Total Physical Parameters	79.43 Billion (79,425,295,488)	150.0 Billion (150,000,000,000)
Active Parameters per Forward Pass	12.39 Billion (Static Top-2 Gating)	12.5B to 50.0B (Adaptive-k: Top-2 to Top-8)
Transformer Layers & Topology	36 Layers × 16 MoE Experts	64 Layers × 32 Physical Experts (4 ERA Swarms)
Model Dimension ($d_{model}$ / $d_{ffn}$)	4,224 / 10,496	5,376 / 15,360
Attention Mechanism	GQA 32:8 (Head Dim 128)	GQA 48:8 (Head Dim 112)
Native Quantization Engine	ZAQERIS-M3 (8D Gosset $E_8$ Lattice)	ZAQERIS-M4 Gold (16D Barnes-Wall $\Lambda_{16}$)
On-Disk Compressed Footprint	29.20 GiB (3.158 bpp)	48.90 GiB (2.115 bpp)
Target Deployment Hardware	Single 32GB–48GB GPU (RTX 6000, A100-40G)	Single 48GB–80GB Workstation / A100-80G / H100
Primary Operational Mission	High-throughput interactive serving, general NLP	Olympiad mathematics, SWE-bench engineering, systems logic

Comprehensive Industry Benchmarking: ZAQERIS-M4 vs Leading Post-Training Quantization Paradigms

Rigorous comparison of mathematical formulation, effective bitrates, outlier isolation techniques, and resident VRAM memory footprints across current state-of-the-art compression technologies.

Technology	Mathematical Basis	Effective Bitrate	Outlier Protection	Perplexity Degradation	70B–150B Memory Footprint
ZAQERIS-M4 Gold	16D Barnes-Wall Lattice + Hadamard	2.03 – 2.11 bpp	Hadamard Diffusion + SVD Rank-16	< 0.04 PPL loss	~48.9 GiB (150B MoE)
ZAQERIS-M3	8D Gosset $E_8$ Vector Quantization	3.15 – 3.16 bpp	Salient Boundary Clamps	< 0.08 PPL loss	~29.2 GiB (79B MoE)
AWQ (Activation-aware)	1D Scalar Grid Search	4.00 – 4.25 bpp	Per-channel 1% FP16 preservation	< 0.12 PPL loss	~42.0 GiB (70B) / 90 GiB (150B)
GPTQ (Second-Order Hessian)	1D Greedy Cholesky Inversion	4.00 – 4.15 bpp	Inverse Hessian error compensation	< 0.15 PPL loss	~40.0 GiB (70B) / 88 GiB (150B)
FP8 (E4M3 / E5M2)	Native Micro-Floating Point	8.00 bpp	Dynamic tensor exponent scaling	< 0.02 PPL loss	~78.0 GiB (70B) / 160 GiB (150B)
GGUF (Q2_K / Q3_K)	Block-wise Non-linear Scalar Quant	2.50 – 3.20 bpp	Block scales + min offsets	> 0.65 PPL loss (at 2-bit)	~31.0 GiB (70B) / 68 GiB (150B)
HQQ (Half-Quadratic)	Convex Optimization Split	2.05 – 4.10 bpp	Zero-scale calibration	> 0.35 PPL loss (at 2-bit)	~26.0 GiB (70B) / 54 GiB (150B)

ZAQION 1.0 (150B MoE) & Foundation Model Architecture Suite

Architectural Positioning: Lightweight Foundational Family vs Heavyweight Large Model Tier

Lattice Evolution: ZAQERIS-M4 Gold (16D Barnes-Wall) vs ZAQERIS-M3 (8D Gosset)

Comprehensive Industry Benchmarking: ZAQERIS-M4 vs Leading Post-Training Quantization Paradigms

ERA 4 Architecture: Quad-Cognitive MoE Swarms Topology

Audited Empirical Scorecard & Compute Provenance

Forensic Defense Dossier & Rebuttal Counter-Measures