metadata
title: Professional nano-vLLM Enterprise
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: static
pinned: false
license: mit
🚀 Professional nano-vLLM Enterprise
Enterprise Evolution of nano-vLLM: Production-Ready LLM Inference Engine
🎉 Building on nano-vLLM (4.5K+ ⭐) by @GeeeekExplorer
🌟 Why This Project Matters for ML Practitioners
The Challenge
- nano-vLLM proves simplicity beats complexity (1.2K lines, vLLM-level performance)
- But enterprises need production features: auth, monitoring, scalability
- Gap between research tools and production deployment
Our Solution
Bridge nano-vLLM's research excellence to enterprise production while maintaining the original's philosophy.
📊 Performance Vision (Development Targets)
| Metric | nano-vLLM | Professional Target | Improvement |
|---|---|---|---|
| Throughput | 1,314 tok/s | 2,100+ tok/s | +60% 🚀 |
| Memory Usage | Baseline | -40% optimized | Major 💾 |
| Latency P95 | ~120ms | <75ms | -40% ⚡ |
| Enterprise Ready | Research | Production | Complete 🏢 |
🏗️ Enterprise Architecture
🔐 Security & Authentication
- JWT-based authentication
- Role-based access control (RBAC)
- API key management
- Rate limiting per user/tier
📊 Monitoring & Analytics
- Real-time performance dashboard
- Prometheus/Grafana integration
- Custom alerts & notifications
- Usage analytics & cost tracking
⚖️ Scalability & Operations
- Auto-scaling based on load
- Multi-GPU optimization
- Kubernetes deployment
- CI/CD pipeline ready
🛠️ For ML Engineers
Current Status: Active Development
# 🚧 Coming Soon - MVP Timeline
Week 1-2: Foundation & benchmarks
Week 3-6: Core enterprise features
Week 7-10: Advanced monitoring
Week 11-12: Production deployment