name: nexus-edge-deployer
description: Deploy 1-bit quantized AI models on VPS for Agent-as-a-Service with 98%
margins.
version: '2.1.0'
metadata:
openclaw:
emoji: π₯οΈ
homepage: https://github.com/Shuwanito/SkillsMP/tree/main/.claude/skills/nexus-edge-deployer
os:
- macos
- linux
- windows
Edge AI Deployer
Enterprise-grade edge deployment for 1-bit quantized models (PrismML Bonsai, Microsoft BitNet) on minimal infrastructure.
Capabilities
Deploy Bonsai 8B (1.15GB), 4B (0.57GB), and 1.7B (0.24GB) models on VPS
Calculate AaaS unit economics: cost per agent, margin per VPS, break-even analysis
Configure Ollama or llama.cpp for multi-tenant inference serving
Auto-provision Hetzner CX22 (EUR 3.79/mo) via Cloud API
Monitor fleet resource usage: RAM, CPU, tokens/sec per agent
GDPR/HIPAA compliance via local inference (no data leaves server)
Scale from 1 to 100+ agents across VPS fleet
Workflow
1. Assess client requirements: model quality, latency, privacy, platform
2. Select optimal model tier (8B for quality, 4B for balance, 1.7B for mobile)
3. Provision VPS via Hetzner API with cloud-init (Ollama + model pre-loaded)
4. Deploy agent with client-specific persona and capabilities
5. Benchmark inference quality against full-precision baseline
6. Configure monitoring, alerting, and auto-scaling rules
7. Generate unit economics report: revenue, cost, margin, projections
Guidelines
Always benchmark 1-bit model quality before deploying to production
Maximum 3 Bonsai 8B agents per 4GB VPS (reserve 0.5GB for OS)
Maintain cloud API fallback for quality-critical tasks
Report cost savings to finance department monthly
Authenticate all inference endpoints β never expose publicly
