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Safebots Infrastructure

License Version Status Models

Trusted, Attested Safebox Infrastructure for Organizations and Businesses to Reproduce and Verify

Production-ready cloud infrastructure specifications with hardware attestation, deterministic builds, and multi-cloud support (AWS, GCP, Azure)

📋 Table of Contents

Overview

Safebots Infrastructure provides trusted, hardware-attested specifications for deploying Safebox environments across cloud providers. Organizations can reproduce and independently verify the entire infrastructure stack — from base OS to AI models — ensuring security, transparency, and compliance.

This repository contains composable infrastructure specifications for building secure, multi-tenant AI/ML environments. Mix and match 18 components to create custom cloud images optimized for your use case.

Key Features

  • 🔐 Hardware Attestation - TPM 2.0 measured boot with verifiable build chain
  • 🔄 Reproducible Builds - Deterministic infrastructure anyone can verify
  • ☁️ Multi-Cloud Ready - AWS (ready), GCP & Azure (planned Q3 2026)
  • 🧩 18 Composable Components - Base + 17 optional modules
  • 🤖 70+ AI Models - Tiny (1.5B) to XL (744B parameters)
  • 🔒 Triple Encryption - Nitro RAM + vTPM + ZFS AES-256-GCM
  • 🎯 Deterministic Inference - Reproducible AI without breaking crypto
  • 📄 Complete Pipelines - PDF/video → vector search
  • ✅ GPL-Free Runtime - 100% permissive licenses
  • 💾 ZFS Storage - Instant snapshots, clones, and workspaces
  • 🗄️ Native MariaDB - File-per-table with ZFS dataset isolation

What's Included

Component Description Size
Base (required) MariaDB, PHP, nginx, Docker, Node.js, ZFS, 50+ npm packages ~8 GB
Media FFmpeg (LGPL), pdfium, libvips, ImageMagick ~370 MB
Vision SigLIP, BiRefNet, SAM 2 ~1.5 GB
Embeddings BGE-M3, Nomic, Jina ~1.5 GB
Speech Whisper Turbo/Large, Silero VAD, Kokoro TTS ~1.2 GB
LLM Tiers Tiny (7.5 GB) to XL (850 GB) Variable

Total: 15 GB (tiny) to 850 GB (XL) depending on configuration

Quick Start

1. Clone Repository

git clone https://github.com/Safebots/Infrastructure
cd Infrastructure/aws    # AWS implementation (GCP/Azure coming Q3 2026)

2. Choose Your Configuration

Development / Edge:

./scripts/build-ami.sh base,llm-tiny
# Result: 15 GB, 8 GB RAM, t3.large

Production (Recommended):

./scripts/build-ami.sh base,media,vision,embed,speech,llm-medium
# Result: 110 GB, 64 GB RAM, r6i.8xlarge

Research / Frontier:

./scripts/build-ami.sh base,media,vision,embed,speech,llm-xl,cuda,vllm
# Result: 850 GB, 256+640 GB RAM, p5.48xlarge

3. Deploy to AWS

# Build your infrastructure image
cd Infrastructure/aws
sudo ./scripts/build-ami.sh base,media,llm-medium

# Launch attested instance
aws ec2 run-instances \
  --image-id ami-xxxxx \
  --instance-type r6i.8xlarge \
  --key-name your-key \
  --metadata-options "HttpTokens=required,InstanceMetadataTags=enabled"

# Verify TPM attestation
./scripts/verify-attestation.sh <instance-id>

4. Run Deterministic Inference

# Set seed for reproducible output
export SAFEBOX_INFERENCE_SEED="my_research_seed_12345"
export LD_PRELOAD=/opt/safebox/lib/libsafebox_deterministic.so

# Run inference (same input + seed = identical output)
echo "Explain quantum computing" | llama-cli --model qwen-3.6-27b-q4.gguf

Architecture

Composable Components

Safebox uses a modular component system. Select only what you need:

graph TD
    A[Base AMI] --> B[Media Processing]
    A --> C[AI/ML Models]
    A --> D[Infrastructure]
    
    B --> B1[FFmpeg]
    B --> B2[pdfium]
    B --> B3[LibreOffice]
    
    C --> C1[Vision Models]
    C --> C2[Embeddings]
    C --> C3[Speech]
    C --> C4[LLM Tiny/Small/Medium/Large/XL]
    
    D --> D1[CUDA]
    D --> D2[vLLM]
    D --> D3[Vector DB]
Loading

Component List:

# Component Size Description
1 base ~8 GB MariaDB, PHP, nginx, Docker, Node.js, ZFS, 50+ npm packages (required)
2 media ~370 MB FFmpeg, pdfium, libvips, ImageMagick
3 libreoffice ~600 MB Office document conversion
4 vision ~1.5 GB SigLIP, BiRefNet, SAM 2
5 vision-hq ~3 GB High-quality vision models
6 embed ~1.5 GB BGE-M3, Nomic, Jina embeddings + rerankers
7 speech ~1.2 GB Whisper Turbo/Large v3, Silero VAD, Kokoro TTS
8 speech-hq ~2 GB Whisper Large v3, high-quality TTS
9 ocr ~50 MB PaddleOCR
10 llm-tiny ~7.5 GB Gemma E2B, Qwen 4B, Phi-mini, Privacy Filter
11 llm-small ~26 GB Qwen 8B, Mistral 12B, Phi-4, Gemma 9B
12 llm-medium ~103 GB Qwen 27B, Gemma 4 31B/26B, Mistral 24B (recommended)
13 llm-large ~169 GB Llama Scout, Qwen 72B, Llama 70B, Nemotron 49B
14 llm-xl ~420 GB GLM-5.1 (MIT), DeepSeek V3.2, Qwen 397B
15 cuda ~3 GB NVIDIA GPU support
16 vllm ~3 GB Batched LLM serving
17 diffusion-small ~8 GB Stable Diffusion (AGPL - flagged)
18 index ~300 MB FalkorDB vector/graph (SSPL - flagged)

LLM Tiers

Complete model lineup by tier:

llm-tiny (~7.5 GB)

  • Gemma 4 E2B 2.3B (Apache 2.0) - Edge, mobile
  • Qwen 3.6 4B (Apache 2.0) - Classification
  • Phi-4-mini 3.8B (MIT) - Small tasks
  • OpenAI Privacy Filter 1.5B (Apache 2.0) - PII redaction (NEW: April 2026)

llm-small (~26 GB)

  • Qwen 3.6 8B (Apache 2.0) - General assistant
  • Mistral Nemo 12B (Apache 2.0) - Reasoning
  • Phi-4 14B (MIT) - Coding
  • Gemma 4 9B (Gemma Terms) - Math, reasoning

llm-medium (~103 GB) ⭐ RECOMMENDED

  • Qwen 3.6 27B (Apache 2.0) - Coding specialist (NEW: April 2026)
    • 77.2% SWE-bench Verified, matches Claude 4.5 Opus
  • Gemma 4 31B Dense (Apache 2.0) - Math/reasoning (NEW: April 2026)
    • 89.2% AIME 2026, #3 open model globally
  • Gemma 4 26B MoE (Apache 2.0) - Efficient (3.8B active) (NEW: April 2026)
    • Beats gpt-oss-120B on GPQA Diamond
  • Qwen 3.6 35B-A3B (Apache 2.0) - MoE, 3B active
  • Mistral Small 4 24B (Apache 2.0) - Unified (vision+code+reasoning)
  • Qwen 3.6 32B (Apache 2.0) - Dense baseline

llm-large (~169 GB)

  • Llama 4 Scout 109B MoE (Llama Community) - 17B active, 10M context
  • Qwen 3.6 72B (Qwen License) - Strong reasoning
  • Llama 3.3 70B (Llama Community) - Baseline 70B
  • Nemotron Super 49B (NVIDIA Open) - High throughput

llm-xl (~420-860 GB)

  • GLM-5.1 744B MoE (MIT) - #1 SWE-bench Pro (NEW: April 2026)
    • Beats GPT-5.4 and Claude Opus 4.6, 8-hour autonomous coding
  • DeepSeek V3.2 685B MoE (MIT) - 32B active
  • Qwen 3.5 397B (Apache 2.0) - Reasoning specialist
  • Llama 4 Maverick 400B MoE (Llama Community) - 17B active, multilingual

Storage Architecture (ZFS + Docker + MariaDB)

Complete storage hierarchy:

/ (ext4, root volume)
├── /boot                           # ext4 (AWS boot requirement)
├── /var/log                        # ext4 (system logs)
└── /srv (ZFS pool: safebox-pool)   # All data on ZFS
    │
    ├── /srv/safebox                # Binaries, models (ZFS dataset)
    │   ├── bin/, lib/, runtimes/, models/
    │   └── manifests/              # Component manifests
    │
    ├── /srv/docker                 # Docker overlay2 (ZFS dataset)
    │   └── overlay2/               # Container layers
    │
    ├── /srv/mariadb                # MariaDB data (ZFS dataset)
    │   ├── data/                   # System tables
    │   ├── tenants/
    │   │   ├── tenant_alice/       # ZFS dataset (quota, encrypted)
    │   │   └── tenant_bob/         # ZFS dataset
    │   └── projects/
    │       ├── project_001/        # ZFS dataset
    │       │   @baseline           # ZFS snapshot (instant)
    │       ├── project_001_exp_a/  # ZFS clone (workspace, zero space)
    │       └── project_001_exp_b/  # ZFS clone (workspace)
    │
    └── /srv/tenants                # Tenant files (ZFS datasets)
        ├── alice/                  # PHP, Node.js files
        └── bob/

Key Features:

  1. ZFS Snapshots - Instant point-in-time copies (milliseconds, zero space)
  2. ZFS Clones - Copy-on-write workspaces (instant, diverge on write)
  3. Per-dataset Encryption - AES-256-GCM, TPM-sealed keys
  4. Quotas - Hard limits per tenant/project
  5. Compression - LZ4 (20-30% space savings, minimal CPU)

Example: Create 10 Experiment Workspaces

# 1. Snapshot baseline (instant, zero space)
zfs snapshot safebox-pool/projects/project_001@baseline

# 2. Create 10 clones (instant, zero initial space)
for i in {a..j}; do
    zfs clone safebox-pool/projects/project_001@baseline \
        safebox-pool/projects/project_001_exp_$i
done

# 3. All experiments use ONE MariaDB server
mysql -e "CREATE DATABASE project_001_exp_a;"
mysql -e "CREATE TABLE project_001_exp_a.data (...) 
    DATA DIRECTORY='/srv/projects/project_001_exp_a';"

Result: 10 isolated workspaces, zero initial space, full isolation!

Why Native (Not Docker) for PHP/Node/MariaDB:

Aspect Native + ZFS Docker
Performance ✅ Direct I/O ❌ Overlay overhead
Instant Workspaces ✅ ZFS clone (ms) ❌ Volume snapshot (slower)
MariaDB Integration ✅ File-per-table + ZFS ❌ Additional abstraction
Complexity ✅ systemd units ❌ Docker Compose
Multi-tenant ✅ Unix users + ZFS quotas ⚠️ Container sprawl

Verdict: Native processes with ZFS = simpler, faster, more flexible

Docker IS used for:

  • AI model serving (vLLM, GPU isolation)
  • Third-party services (Redis, Elasticsearch)
  • Sandboxed execution (untrusted code)

Deterministic Inference

Goal: Reproducible AI inference WITHOUT breaking OpenSSL/TLS/crypto security

Solution: libsafebox_deterministic.so - LD_PRELOAD wrapper for AI processes only

// Intercepts /dev/urandom reads for AI model runners
// ChaCha20 PRNG (same as Linux kernel)
// Thread-safe, production-ready

Usage:

# AI inference - deterministic
export SAFEBOX_INFERENCE_SEED="research_seed_12345"
export LD_PRELOAD=/opt/safebox/lib/libsafebox_deterministic.so
llama-server --model qwen-3.6-27b-q4.gguf

# Everything else - real randomness (no LD_PRELOAD)
nginx      # TLS handshakes use real kernel entropy
php-fpm    # Session tokens use real entropy
mariadb    # UUIDs use real entropy

Benefits:

  • ✅ Same seed + same input = bitwise identical output
  • ✅ Crypto stays secure (real entropy for TLS, sessions, UUIDs)
  • ✅ TPM/Nitro attestation preserved
  • ✅ Multi-tenant safe (per-process seeds)

Features

April 2026 Model Updates

Five major additions:

Model License Size Highlight
OpenAI Privacy Filter Apache 2.0 1.5B PII redaction (96% accuracy), 128K context, browser-ready
Qwen 3.6 27B Apache 2.0 27B Coding: 77.2% SWE-bench, matches Claude 4.5 Opus
Gemma 4 31B Apache 2.0 31B Math: 89.2% AIME 2026, #3 open model globally
Gemma 4 26B MoE Apache 2.0 26B (3.8B active) Efficient, beats gpt-oss-120B
GLM-5.1 MIT 744B (40B active) #1 SWE-bench Pro, 8-hour autonomous coding

License Revolution:

  • ✅ Gemma 4: Google's first Apache 2.0 (previously restrictive)
  • ✅ GLM-5.1: MIT (most permissive for XL tier)
  • ✅ All medium tier: 100% Apache 2.0 or Gemma Terms

Document & Video Ingestion

PDF Pipeline:

PDF page → pdfium (render + text) → density check
├─ High density → chunk + BGE-M3 embed
├─ Low density → PaddleOCR → embed
├─ Visual: SigLIP 2 → tags + embeddings
└─ Optional: ColQwen2 multi-vector embeddings

Video Pipeline:

Video → PySceneDetect → keyframes
├─ Keyframes → SigLIP → visual embeddings per scene
├─ Audio → Silero VAD → Whisper → transcript embeddings
└─ Optional: InternVideo2 spatiotemporal embeddings

Result: Four parallel retrieval surfaces per document/video

NPM Package Catalog

50+ packages in base AMI (~200 MB):

Category Packages
Document docx, exceljs, xlsx, pptxgenjs, officegen, mammoth
PDF pdfkit, pdf-lib, jspdf, html-pdf-node
Images sharp, jimp, canvas, qrcode, svg-captcha
Charts chartjs-node-canvas, mermaid, d3-node, vega
Archives archiver, adm-zip, tar-stream
Email nodemailer, mjml

All licenses: MIT, Apache 2.0, BSD, ISC

Cascading Manifest System

Auto-discovery at boot:

/opt/safebox/manifests/
├── base.json          # Always present (npm packages + core)
├── media.json         # FFmpeg/pdfium capabilities
├── llm-medium.json    # 6 LLM models + capabilities
└── _merged.json       # Generated at boot (deep-merged)

Manifest structure:

{
  "component": {
    "name": "llm-medium",
    "license": ["Apache-2.0"],
    "disk": "103 GB"
  },
  "models": { /* model definitions */ },
  "capabilities": {
    "Safebox/capability/llm/code": {
      "provider": "com.safebox.local",
      "runtime": "llama.cpp",
      "model": "qwen-3.6-27b-q4"
    }
  }
}

Documentation

Core Documents

Document Lines Description
README.md - This file
SAFEBOX-COMPLETE-SUMMARY.md 500+ Complete architecture overview
SAFEBOX-FINAL-APRIL-2026.md 454 Latest model updates

Technical Deep Dives

Document Lines Description
DETERMINISTIC-AI-ONLY-RNG.md 545 LD_PRELOAD solution, ChaCha20 implementation
SAFEBOX-ZFS-DOCKER-MARIADB-ARCHITECTURE.md 593 Complete storage architecture
CASCADING-MANIFESTS.md 1200 Manifest auto-discovery system
NPM-PACKAGES-CATALOG.md 1008 50+ package catalog with examples

Architecture Specifications

Document Description
SAFEBOX-COMPOSABLE-ARCHITECTURE.md 18-component system specification
safebox-packages.json JSON schema for all packages

Build System

Master Build Script

./scripts/build-ami.sh <component-list>

Examples:

# Tiny configuration
./scripts/build-ami.sh base,llm-tiny

# Medium configuration (recommended)
./scripts/build-ami.sh base,media,vision,embed,llm-medium

# Full XL configuration
./scripts/build-ami.sh base,media,vision,embed,speech,llm-xl,cuda,vllm

Component Installers

Each component has an installer script:

scripts/components/
├── base/install-base.sh
├── media/install-media.sh
├── llm-medium/install-llm-medium.sh
└── ...

Installer responsibilities:

  1. Install binaries/models
  2. Generate manifest JSON
  3. Validate licenses
  4. Update boot services

Security

Hardware Attestation Chain

  1. AWS Nitro System - Hardware root of trust
  2. vTPM 2.0 - Measured boot with PCR values
  3. Deterministic Build - Reproducible from source
  4. Public Verification - Anyone can verify the build chain

Attestation Flow:

Source Code → Deterministic Build → AMI Hash → TPM PCRs → Remote Attestation

Triple-Layer Encryption

  1. AWS Nitro Enclave - Hardware RAM encryption
  2. vTPM 2.0 - Measured boot with attestation chain
  3. ZFS AES-256-GCM - Per-dataset encryption, TPM-sealed keys

Deterministic Inference Security

  • ✅ AI-only: LD_PRELOAD wrapper for model runners
  • ✅ Crypto-safe: OpenSSL/TLS use real kernel entropy
  • ✅ TPM-compatible: Attestation chain preserved
  • ✅ Multi-tenant: Per-process seed isolation

Network Isolation

  • Native processes (not Docker) for critical services
  • systemd cgroups for resource limits
  • chroot for filesystem isolation
  • ZFS datasets with quotas

License Compliance

Runtime Components

Component License Notes
NPM packages MIT, Apache 2.0, BSD, ISC All permissive
System tools LGPL 2.1+ (dynamic), BSD, MPL 2.0 Dynamic linking only
Media toolchain LGPL 2.1+, BSD, ImageMagick GPL-free build

AI Models

Tier Licenses Notes
Tiny/Small/Medium Apache 2.0, MIT, Gemma Terms 100% permissive
Large Apache 2.0, Llama Community, Qwen, NVIDIA Open Llama: 700M MAU restriction
XL MIT, Apache 2.0, Llama Community GLM-5.1 is MIT!

Key Wins

  • ✅ Gemma 4: Apache 2.0 (Google's first!)
  • ✅ GLM-5.1: MIT (most permissive XL)
  • ✅ All medium tier: 100% permissive
  • ✅ Zero GPL in runtime path

Flagged Components

  • ⚠️ diffusion-small: AGPL (Stable Diffusion) - Flagged for tenants
  • ⚠️ index: SSPL (FalkorDB) - Acceptable self-hosted, flagged for SaaS

Performance Benchmarks

Model Performance (April 2026)

Qwen 3.6 27B:

  • SWE-bench Verified: 77.2% (beats 397B model)
  • Terminal-Bench 2.0: 59.3% (matches Claude 4.5 Opus)
  • QwenWebBench: 1487

Gemma 4 31B:

  • AIME 2026: 89.2% (math reasoning)
  • GPQA Diamond: 84.3% (science reasoning)
  • LiveCodeBench v6: 80.0%
  • Arena AI: #3 globally among open models

GLM-5.1 744B:

  • SWE-bench Pro: 58.4% (#1, beats GPT-5.4 and Claude Opus 4.6)
  • 8-hour autonomous coding capability
  • Trained entirely on Huawei chips (zero NVIDIA)

Ingestion Throughput

  • PDF: 5-10 pages/sec (pdfium + BGE-M3)
  • Video: 1-2 scenes/sec (PySceneDetect + SigLIP)
  • Audio: 10x realtime (Whisper Turbo)

Quantization Impact

Format Size Quality Loss Speed
FP16 100% 0% Baseline
Q8_0 50% <1% 1.2x
Q6_K 37.5% <2% 1.5x
Q4_K_M 25% <5% 2.0x

Recommended: Q4_K_M for medium/large, Q6_K for XL

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone repository
git clone https://github.com/your-org/safebox-ami-spec
cd safebox-ami-spec

# Install dependencies
./scripts/install-dev-deps.sh

# Build test AMI
sudo ./scripts/build-ami.sh base,llm-tiny

# Run tests
./scripts/run-tests.sh

Areas for Contribution

  • 📦 New component modules
  • 🤖 Additional AI model integrations
  • 📝 Documentation improvements
  • 🧪 Testing and validation
  • 🔧 Build system enhancements

Roadmap

Q2 2026 (Current)

  • ✅ 18-component composable architecture
  • ✅ 70+ AI models integrated
  • ✅ Deterministic inference (LD_PRELOAD)
  • ✅ Complete ingestion pipelines
  • ✅ GPL-free guarantee
  • ✅ ZFS storage architecture

Q3 2026

  • Llama 4 Scout integration (10M context)
  • Real-time streaming inference
  • Multi-modal unified embeddings
  • Enhanced workspace management UI

Q4 2026

  • Cross-lingual video search
  • Edge optimization (Q3_K_M quantization)
  • Kubernetes deployment support
  • Advanced cost optimization

Support

Project Status

  • Version: 1.0.0
  • Status: Production-Ready
  • Last Updated: April 23, 2026
  • Models: 70+ (all permissive licenses)
  • Components: 18 (mix-and-match)
  • Documentation: 6,000+ lines

Acknowledgments

AI Models

  • Google DeepMind (Gemma 4)
  • Alibaba Qwen Team (Qwen 3.6)
  • Zhipu AI / Z.ai (GLM-5.1)
  • OpenAI (Privacy Filter)
  • Meta (Llama 4)
  • Mistral AI (Mistral Small 4)

Open Source Projects

License

This project uses multiple licenses:

  • Core framework: Apache 2.0
  • AI models: Apache 2.0, MIT, BSD, custom permissive (see LICENSE-MODELS.md)
  • Media tools: LGPL 2.1+ (dynamic link), BSD, MPL 2.0 (see LICENSE-MEDIA.md)
  • Documentation: CC BY 4.0

GPL-free guarantee: No GPL dependencies in runtime path.

See LICENSE for full details.

Safebots Infrastructure
Trusted, Attested Infrastructure for Organizations to Reproduce and Verify

Built with ❤️ for secure, reproducible, multi-tenant AI workloads

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