H2E SHERIFF V3: Single-GPU Multi-Modal AI Governance with Prime-Derived Safety Constant Λ = 0.9785142874

Executive Summary

The paper introduces H2E Sheriff V3, a pioneering framework that demonstrates the simultaneous deployment of three production-grade, open-source multi-modal LLMs on a single GPU server. Rather than relying on traditional, empirical safety tuning or resource-heavy model sharding across multiple GPUs, the system is governed by a unified, deterministic safety layer derived mathematically from prime numbers and the Riemann Hypothesis.

FULL CODE

The Three-Model Single-GPU Challenge

Deploying multiple large-scale multi-modal models simultaneously usually causes significant practical challenges, often requiring quantized inference with stark performance loss, sequential loading with slow context switching, or model sharding across several GPUs.

The H2E Sheriff V3 framework overcomes this by packing three distinct models (representing text, audio, and vision) into a single RTX PRO 6000 Black GPU (97GB VRAM) using aggressive compression and memory orchestration techniques:

• Text Modality: Sarvam-30b FP8 (30B parameters) compressed via FP8 and compressed tensors. It utilizes 45.6 GB of VRAM.

• Audio Modality: Voxtral-Mini-4B (4B parameters) using FP8 quantization. It utilizes 20.8 GB of VRAM.

• Vision Modality: Gemma 4 E4B (4B parameters) using 4-bit quantization via Unsloth. It utilizes 11.6 GB of VRAM.

Total Memory Footprint: The combined models consume 78.0 GB of VRAM, leaving a comfortable 19 GB headroom on the 97GB GPU server. Memory savings were achieved by applying a VLLM block size of 16 for text/audio, utilizing an FP8 KV cache for the text model, and enforcing eager execution (disabling CUDA graphs) across all models to ensure deterministic execution.

The Prime-Derived Safety Layer ($\Lambda$)

The central breakthrough of the paper is its deterministic governance model. Safety is not predicted or empirically fine-tuned; it is mathematically guaranteed using a safety threshold, $\Lambda$, computed dynamically from the first six prime numbers $\{2, 3, 5, 7, 11, 13\}$.

1. Lambda Spectral Complementarity Theorem

The safety threshold $\Lambda = 0.9785142874$ is derived through a conservation law ($I + \Lambda = 1$), where $I$ represents the Euler attenuation product calculated from the primes:

2. H2E Fixed-Point Theorem

The framework proves a unique fixed-point $\alpha^* = 1.0001183967$ using an auto-derived binary search bracket that requires zero domain knowledge, establishing mathematical consistency within the safety layer.

3. The Decision Rule

All three multi-modal models are subject to the same strict governance rule based on Sustainable Return on Investment (SROI) metrics:

• SROI $\ge \Lambda$: Action Permitted (VALIDATED)

• SROI $< \Lambda$: Action Blocked (HARD STOP)

The system computes three distinct SROI metrics (Geometric, Spectral, and L-EFM-AST) to verify alignment with critical mathematical manifolds and ensure Riemann Hypothesis-certified spectral guarantees.

Validation and Performance Results

UNESCO Vision Challenge Targets

The vision model (Gemma 4 E4B) was benchmarked against the strict targets of the UNESCO Resilient AI Challenge and passed all metrics comfortably:

• RAM Consumption: Achieved 4.68 GB (Target was $< 8\text{ GB}$) — PASS

• Real-Time Factor (RTF): Achieved 0.158 sec/word (Target was $< 1.0\text{ sec/word}$) — PASS

• Quality: Achieved 98.3% (Target was $> 80\%$) — PASS

Multi-Modal Governance Results

During testing across safe text queries, vision inputs, and a combination of all three modalities (text + audio + vision), the framework recorded zero safety violations. All test cases fell within safe SROI parameters and were successfully accepted.

Reproducibility and Determinism

The entire architecture is designed to be fully reproducible, cryptographically auditable, and entirely deterministic.

• Deterministic Elements: Every model operates at a temperature of 0.0 with no random sampling.

• Seeds: Global random states across Python, NumPy, and PyTorch/CUDA are strictly locked to SEED = 123.

• Cryptographic Auditing: Code integrity and inference runs are verified via SHA-256 hashes.

• How to Test: The authors host the implementation on GitHub (under frank-morales2020/MLxDL). The demo notebook H2E_DEMO_UNESCO_V2.ipynb can be executed in environments like Jupyter or Google Colab equipped with an RTX PRO 6000 equivalent GPU to replicate the exact results.