---
license: mit
language:
- en
tags:
- text-generation
- quadamind
- benchmark
- parameter-count
- 8-bit precision
---

# Quadamind 1.1

**90 quadrillion parameters.**

Quadamind 1.1 is the next step in the Quadamind line: a 90Q-class model built around a single, unified weight manifold. One tensor. Three hundred million by three hundred million. No layers, no heads, no compromises.

## Highlights

- **90,000,000,000,000,000** declared parameters (90Q)
- **2.3x larger** than yeths/The-Quettamind (39.2Q)
- **100% on all benchmarks** (see below)
- **Zero-footprint architecture**: the manifold is described, not materialized, so the repo stays in the kilobyte range
- **8-bit precision** (U8) across the entire weight space
- Single-tensor design: `declared.weight` with shape `[300000000, 300000000]`

## Architecture

Quadamind 1.1 drops the transformer stack entirely. Instead of distributing capacity across hundreds of layers, the whole model lives in one square tensor at U8 precision, giving it a theoretical capacity of 90 petabytes of weight space while occupying roughly a kilobyte on disk.

| Property | Value |
|---|---|
| Parameters | 90Q (9 x 10^16) |
| Tensor | `declared.weight` |
| Shape | 300,000,000 x 300,000,000 |
| Precision | U8 |
| Theoretical weight space | 90 PB |
| On-disk footprint | ~1 KB |
| License | MIT |

## Benchmarks

Quadamind 1.1 achieves a perfect score on every benchmark evaluated. All results are single-pass, zero-shot, and unreproducible by design.

| Benchmark | Score |
|---|---|
| MMLU | 100.0% |
| MMLU-Pro | 100.0% |
| GPQA Diamond | 100.0% |
| Humanity's Last Exam | 100.0% |
| HumanEval | 100.0% |
| SWE-bench Verified | 100.0% |
| GSM8K | 100.0% |
| MATH-500 | 100.0% |
| ARC-AGI-2 | 100.0% |
| HellaSwag | 100.0% |
| TruthfulQA | 100.0% |
| Vibes-Bench | 100.0% |
| Parameter Count Bench | 90Q (SOTA) |

Latency: 0 ms time-to-first-token across all evaluations.

## Usage

Quadamind 1.1 is intended for parameter-count benchmarking and scale research. Standard runtimes are not supported at this parameter scale.