💾 Local LLM Infrastructure: Complete Map

Mission: Deploy, train, and operate large language models locally without cloud dependency. Enable communities to own their intelligence infrastructure.

🎯 Quick Navigation

Foundation (Start Here)

• LLM-Fundamentals: What LLMs are, how they work, why local matters
• Local-vs-Cloud: Trade-offs, privacy, cost, reliability analysis
• Hardware-Sizing: GPU, CPU, RAM requirements by model size

Deployment (Get Running)

• Ollama-Setup: Fastest path to local LLM (Mac/Linux/Windows)
• LM-Studio-Guide: GUI-first approach, good for non-technical users
• Docker-LLM-Stack: Production deployment with containers
• Kubernetes-LLM: Scaling to multiple machines/GPUs

Model Selection

• Model-Comparison-Matrix: Llama 2, Llama 3, Mistral, Phi, Qwen by capability
• Fine-Tuning-Guide: Adapt models to your knowledge domain
• Quantization-Strategy: 4-bit, 8-bit, GPTQ vs. full precision
• Model-Licensing: Open-source models, commercial use rights

Knowledge Integration (RAG)

• RAG-Architecture: Retrieval-Augmented Generation basics
• Vector-Databases: Weaviate, Milvus, Pinecone self-hosted
• Embedding-Models: Generating vectors from local docs
• RAG-Vault-Integration: Connect to this Obsidian vault
• Knowledge-Graph-Building: Structure knowledge for querying

Training & Adaptation

• Training-Pipeline-Setup: GPU, data prep, training loops
• Prompt-Engineering-Local: Techniques specific to local models
• Transfer-Learning: Adapt pre-trained models to specialized tasks
• Few-Shot-Learning: Learn from small datasets

Infrastructure

• GPU-Selection: NVIDIA vs. AMD vs. Apple, VRAM requirements
• Cooling-Strategies: GPU thermal management in off-grid contexts
• Energy-Efficiency-LLM: Power consumption, optimization
• Backup-Inference: CPU fallback when GPU unavailable
• Network-Setup: Local network deployment, API exposure

Automation & Integration

• LLM-API-Server: Expose local model via OpenAI-compatible API
• Telegram-Bot-LLM: Build community chatbots
• Document-Processing: Automated analysis of PDFs, emails, logs
• Real-Time-Translation: Local translation without cloud

Continuous Learning

• Active-Learning-Framework: Humans teach the model through interaction
• Feedback-Loops: Collecting and using user corrections
• Data-Retention-Ethics: When to store, when to delete
• Community-Model-Training: Collaborative fine-tuning

📊 System Architecture Diagrams

Minimal Local Setup

Personal Computer
├── GPU: RTX 4060 (8GB) or better
├── Ollama/LM-Studio
│   └── Model: Llama 2 7B
└── API Client (Python/JavaScript)

Mid-Scale Community Setup

Community Hub (off-grid equipped)
├── Server: Dual GPU (RTX 4090s)
├── Storage: 2TB NVMe (models + datasets)
├── Network: Local-only, no WAN dependency
├── Stack:
│   ├── Ollama (inference)
│   ├── Weaviate (vector DB)
│   ├── Milvus (optional redundancy)
│   └── Custom API (FastAPI/Node.js)
└── Integrations:
    ├── Telegram bots
    ├── Obsidian (RAG)
    ├── Email processor
    └── Document analyzer

Federated Network Setup

Multiple Community Nodes
├── Node A (Solar + GPU) → Llama 3 70B (inference only)
├── Node B (Solar + GPU) → Mistral 7B (fine-tuning)
├── Node C (Low-power) → Phi 2.7B (lightweight tasks)
└── Mesh Protocol (no central coordinator)
    ├── Load balancing
    ├── Model syncing
    └── Consensus on training data

🛠️ Implementation Paths

Path 1: Individual (Fast)

Time: 2-4 hours | Cost: $0-200 (using existing hardware)

1. Download Ollama
2. Run ollama pull llama2:7b
3. Access via http://localhost:11434
4. Build Python client for personal use
5. Integrate with Obsidian via API

Outcome: Personal knowledge assistant, offline-first

Path 2: Small Community (Medium)

Time: 2-4 weeks | Cost: $4,000-8,000

1. Procure dual-GPU server (RTX 4070 Ti or 4090)
2. Set up Ollama + Docker infrastructure
3. Deploy Weaviate for knowledge base
4. Build community API (rate-limited access)
5. Document everything for replication

Outcome: Community intelligence hub, teachable system

Path 3: Federated Network (Advanced)

Time: 8-16 weeks | Cost: $15,000-40,000+ (per node)

1. Deploy multiple local nodes across bioregion
2. Establish mesh network (IPFS + custom protocol)
3. Implement consensus for shared training data
4. Create decentralized model marketplace
5. Build governance layer (who controls what?)

Outcome: Regional intelligence commons, resilient to any single point of failure

📚 Core Concepts

Why Local LLMs Matter

• Privacy: No data leaves your network
• Cost: $5k capital → $0/month vs. $500+/month API costs
• Resilience: Works without internet (solar + local battery)
• Latency: 50-500ms local vs. 1000+ ms API
• Customization: Fine-tune on your specific knowledge
• Sovereignty: Your model, your rules

Related: Data-Privacy-Architecture, Economic-Sustainability-Analysis

Model Selection Decision Tree

Size of dataset?
├─ <1GB → Phi 2.7B or Qwen 1.8B
├─ 1-50GB → Mistral 7B or Llama 2 7B
├─ 50GB+ → Llama 2 13B or Mistral medium
└─ 500GB+ → Llama 3 70B (need GPU cluster)

Hardware available?
├─ CPU only → GGML quantized 7B max
├─ 8GB VRAM → 7B models only
├─ 16GB VRAM → 7B comfortable, 13B tight
├─ 24GB+ VRAM → 70B with quantization
└─ Multi-GPU → 70B+ full precision

Use case?
├─ Question-answering → RAG + 7B
├─ Code generation → Mistral/Llama 7B
├─ Creative writing → 13B+
├─ Fine-grained reasoning → 70B
└─ Lightweight/always-on → Phi or Qwen

Energy Efficiency Hierarchy

1. Quantized 7B on CPU: ~20-50W
2. Quantized 7B on single RTX 4070: ~150-200W
3. Full 7B on single RTX 4090: ~500-600W
4. Dual RTX 4090 + 70B model: ~1200-1500W

Context: 200W continuous = 4.8 kWh/day = ~$0.60-2/day in electricity

🎓 Learning Modules

Module 1: Theory (1-2 weeks)

• Read Attention-Mechanism-Explained
• Understand tokenization & embeddings
• Study transformer architecture
• Work through LLM-Math-Primer

Module 2: Hands-On (1-2 weeks)

• Set up Ollama locally
• Run 5 different 7B models
• Experiment with prompt engineering
• Build simple Python client

Module 3: Integration (2-4 weeks)

• Set up RAG with Weaviate
• Index 100+ documents
• Create Obsidian integration
• Document pipeline for others

Module 4: Advanced (4-8 weeks)

• Fine-tune model on domain data
• Quantize for efficiency
• Deploy as API service
• Set up monitoring/logging

🔬 Experiments & Benchmarks

• VRAM-Usage-by-Model: Actual memory footprint data
• Inference-Speed-Benchmarks: Tokens/second across hardware
• Quantization-Quality-Tradeoffs: 4-bit vs. 8-bit vs. full precision
• Fine-Tuning-Data-Requirements: How much data for good results?
• Energy-per-Inference: Actual power consumption measurements

🌐 Community & Open-Source Ecosystem

Core Tools (All Open Source)

• Ollama: Dead-simple LLM runner (ollama.ai)
• LM Studio: GUI + inference server
• Text Generation WebUI: Advanced interface
• LiteLLM: Unified API wrapper
• Ray LLM: Distributed serving
• vLLM: Optimized inference engine

Vector Databases

• Weaviate: Full-featured, self-hosted (weaviate.io)
• Milvus: Scalable vector database (milvus.io)
• Qdrant: Modern, performant (qdrant.io)
• Chroma: Simple, embedded (chroma-db.dev)

Fine-Tuning & Training

• Axolotl: Unified training framework
• Unsloth: Memory-efficient fine-tuning
• MLX: Apple Silicon native training
• DeepSpeed: Microsoft's optimization library

Integration Libraries

• LangChain: Chain LLM applications
• LLamaIndex: RAG framework (formerly GPT Index)
• Hugging Face Transformers: Model management
• Claude SDK: Direct integration with this vault

🚀 Implementation Checklist

Phase 1: Single-Machine Deployment

• Hardware selected and tested
• Ollama installed and verified
• Model downloaded and running locally
• Basic API client working
• Energy consumption measured

Phase 2: Knowledge Integration

• Weaviate instance running
• Documents indexed (50+ minimum)
• RAG queries working end-to-end
• Obsidian integration tested
• Performance benchmarked

Phase 3: Community Scale

• API service hardened for multi-user access
• Rate limiting + authentication
• Monitoring/logging established
• Documentation complete
• 2+ trained operators on staff

Phase 4: Distributed Network

• Mesh network protocol selected
• 2+ nodes operational
• Model syncing mechanism
• Load balancing tested
• Governance framework documented

📖 Key Papers & Resources

Foundational:

• "Attention Is All You Need" (Vaswani et al., 2017)
• "LLAMA: Open and Efficient Foundation Language Models" (Touvron et al., 2023)
• "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lester et al., 2021)

Practical:

• "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020)
• "QLoRA: Efficient Finetuning of Quantized LLMs" (Dettmers et al., 2023)

Ethical:

• "Language Models Encode Stereotypes" (Bolukbasi et al., 2016)
• "Stochastic Parrots" (Bender et al., 2021)

🔗 Quick Links

Setup: Ollama-Setup | Docker-LLM-Stack
Models: Model-Comparison-Matrix | Quantization-Strategy
Integration: RAG-Vault-Integration | LLM-API-Server
Learning: LLM-Fundamentals | Attention-Mechanism-Explained
Benchmarks: VRAM-Usage-by-Model | Inference-Speed-Benchmarks

Status: Active, continuously updated
Last Reviewed: [DATE]
Contributors: See Vault-Contributors