turboprivate-ai 0.1.4

Unified platform for self-hosted LLM inference + enterprise safety governance

TurboPrivate AI — Private & Safe Enterprise AI Platform

Run powerful LLMs on your own hardware — 40–60% cheaper than public clouds, with built-in enterprise safety & governance.

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Why TurboPrivate AI?

• Full data sovereignty — nothing leaves your infrastructure
• Dramatic cost reduction — INT4/AWQ quantization + smart routing
• Enterprise Safety — powered by Mythos Safe (defensive evaluation, jailbreak protection, audit)
• OpenAI compatible — drop-in replacement for your existing applications
• One-command deploy — from bare metal to production in minutes

Key Features

• TurboQuant Engine — State-of-the-art INT4/AWQ quantization with minimal quality loss
• Mythos Safe — Multi-layer defensive safety (pre & post-flight gates)
• Private RAG — Secure document ingestion and retrieval
• Full-stack observability — Prometheus, Grafana, OpenTelemetry
• Enterprise ready — RBAC, audit trail, multi-tenancy, compliance support
• Hardware flexibility — RTX 4090, A100/H100, or even CPU-only

Performance (RTX 4090)

Model	Quant	Tokens/sec	VRAM Usage	Cost vs Groq/AWS
Llama 3.1 8B	INT4	110+	~5.8 GB	~8x cheaper
Qwen2.5 32B	INT4	45+	~22 GB	~6x cheaper
Llama 3.1 70B	INT4	18+	~48 GB	~5x cheaper

Quick Start

# 1. Deploy full stack (K8s)
turbo deploy --provider bare-metal --gpu auto

# 2. Serve model
turbo model serve meta-llama/Llama-3.1-8B --quant int4

# 3. Chat
turbo chat

Or use Docker Compose for quick testing:

docker compose up -d                    # dev
# docker compose -f docker-compose.prod.yml up -d  # production (GPU)

Pricing

Tier	Price	Best For	Includes
PoC / Pilot	€15,000 – €35,000	4–8 weeks trial	Deployment, 2 models, training, support
Enterprise License	€65,000 / year	Single cluster, up to 10 users	Full features, unlimited models, SLA 99.5%
Enterprise Plus	€120,000 – €180,000 / year	Multiple clusters, 50+ users	Priority support, custom verifiers, SOC2
Managed Service	€8,000 – €25,000 / month	No ops team	Fully managed by us

Volume discounts available for 3+ clusters.
All prices exclude hardware.

Interested in a private demo?
📅 Book a 30-min PoC Call | ✉️ Contact Sales

Architecture

CLI / SDK / Dashboard
        ↓
   API Gateway (FastAPI · Auth · Rate Limiting)
        ↓
┌─────────────────┐  ┌───────────────────┐
│  Mythos Safe    │  │  TurboQuant INT4  │
│  Verifiers ·    │  │  vLLM/llama.cpp   │
│  Audit Trail    │  │  Inference Engine │
└─────────────────┘  └───────────────────┘
        ↓
   Memory & RAG (TurboMemory · pdf2struct)
        ↓
┌──────────┐ ┌──────────┐ ┌──────────┐
│  K3s     │ │Monitoring│ │ Storage  │
│  Cluster │ │Prom/Graf │ │ PG/Redis │
└──────────┘ └──────────┘ └──────────┘

Demo

Documentation

• Architecture — Full system design
• Deployment — Production deployment guide
• CLI Reference — All CLI commands
• API Reference — FastAPI routes
• Safety Gate — Verifier configuration
• Demo Assets — GIF recording tape + deploy script

Changelog

0.1.4 (2026-05-13)

• Production-hardened Helm charts (configmap, ingress, services templates)
• Enhanced rate limiter with token bucket algorithm + per-route limits
• Improved safety gate middleware with pre/post-flight hook chain
• Realtime metrics visualization in dashboard endpoint
• TurboQuant v3 quantization pipeline: AWQ + INT4 mixed-precision
• Backup/restore CLI with age-encrypted snapshots
• K3s provisioner with multi-node discovery + node labels
• vLLM backend: speculative decoding toggle + prefix caching
• llama.cpp backend: flash attention + GPU offloading
• Worker refinements: quantize retry, eval timeout, ingestion dedup
• CLI enhancements: model status, deploy progress, backup summary
• PII detector regex expansion (passport, SSN, phone variants)
• Vulnerability verifier: CVE-2025 scoring + dependency jail status
• PDF/image ingestion with OCR fallback in RAG pipeline

0.1.3 (2026-05-13)

• Extended demo GIF to 61s with 5-scene animation (intro, deploy, serve+chat, safety block, dashboard)
• Switched README GIF to absolute GitHub raw URL for PyPI rendering

0.1.2 (2026-05-11)

• Enterprise-ready README with pricing table and benchmarks
• Added docs/ARCHITECTURE.md with system design diagrams
• Added docs/DEPLOYMENT.md with production deployment guide
• Added examples/ with HTTP, safety, RAG, and quantization samples
• Added .env.example with all configuration options
• Added benchmarks/ with RTX 4090 performance results
• Switched license from MIT to Apache 2.0
• Added turbo doctor CLI command for system health checks
• Added GitHub Actions Docker build workflow
• Updated pyproject.toml with full install extra

0.1.1 (2026-05-11)

• Migrated to hatchling build system
• Fixed missing InferenceEngine import in turbo.inference
• Fixed TracerProvider bug in OpenTelemetry instrumentation
• Added structured logging to all exception handlers
• Consolidated Celery workers into shared worker.celery_app
• Added CI workflow with ruff linting + pytest
• Improved graceful shutdown (audit trail flush)
• Updated dependencies (replaced unstructured with actual used libs)

License

Apache 2.0 — see LICENSE.

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Built by Kubenew — ex-HPE engineer, 12+ years enterprise infrastructure