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Apple df1cd614cd docs: update GPU specs (RTX 4000 Ada) and Vision Encoder performance with GPU acceleration

2025-11-17 11:39:31 -08:00

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🖥️ System Inventory — DAARION & MicroDAO

Version: 1.0.0
Last Updated: 2025-01-17
Server: GEX44 #2844465 (Hetzner)

🖥️ Hardware Specifications

Production Server (144.76.224.179)

Provider: Hetzner Dedicated Server GEX44
Server ID: #2844465

GPU Configuration

GPU Model: NVIDIA RTX 4000 SFF Ada Generation
VRAM: 20 GB GDDR6
Architecture: Ada Lovelace
CUDA Version: 12.2
Driver Version: 535.274.02

Current VRAM Usage:

Ollama (qwen3:8b): ~5.6 GB
Vision Encoder (ViT-L/14): ~1.9 GB
Total: ~7.5 GB / 20 GB (37.5% usage)

CPU & RAM (Typical GEX44)

CPU: AMD Ryzen 9 5950X (16 cores, 32 threads) or similar
RAM: 128 GB DDR4
Storage: 2x NVMe SSD (RAID configuration)

🤖 Installed AI Models

1. LLM Models (Language Models)

Ollama (Local)

Service: Ollama
Port: 11434
Status: ✅ Active

Installed Models:

Model	Size	Parameters	Context	VRAM Usage	Purpose
qwen3:8b	~4.7 GB	8B	32K	~6 GB	Primary LLM for Router, fast inference

API:

# List models
curl http://localhost:11434/api/tags

# Generate
curl http://localhost:11434/api/generate -d '{
  "model": "qwen3:8b",
  "prompt": "Hello"
}'

Configuration:

Base URL: http://172.17.0.1:11434 (from Docker containers)
Used by: DAGI Router, DevTools, CrewAI, Gateway

2. Vision Models (Multimodal)

OpenCLIP (Vision Encoder Service)

Service: vision-encoder
Port: 8001
Status: ✅ Active (GPU-accelerated)

Model Details:

Model	Architecture	Parameters	Embedding Dim	VRAM Usage	Purpose
ViT-L/14	Vision Transformer Large	~428M	768	~4 GB	Text/Image embeddings for RAG
OpenAI CLIP	CLIP (Contrastive Language-Image Pre-training)	-	768	-	Pretrained weights

Capabilities:

✅ Text → 768-dim embedding (0.1-0.5s on GPU, ~10-15s on CPU)
✅ Image → 768-dim embedding (0.3-1s on GPU, ~15-20s on CPU)
✅ Text-to-image search (via Qdrant)
✅ Image-to-image similarity search (via Qdrant)
✅ GPU acceleration: ~20-30x speedup vs CPU
⏳ Zero-shot image classification (planned)
⏳ CLIP score calculation (planned)

API Endpoints:

# Text embedding
POST http://localhost:8001/embed/text

# Image embedding (URL)
POST http://localhost:8001/embed/image

# Image embedding (file upload)
POST http://localhost:8001/embed/image/upload

# Health check
GET http://localhost:8001/health

# Model info
GET http://localhost:8001/info

Configuration:

Model: ViT-L-14
Pretrained: openai
Device: cuda (GPU)
Normalize: true
Integration: DAGI Router (mode: vision_embed, image_search)

3. Embedding Models (Text)

BAAI/bge-m3 (RAG Service)

Service: rag-service
Port: 9500
Status: ✅ Active

Model Details:

Model	Type	Embedding Dim	Context Length	Device	Purpose
BAAI/bge-m3	Dense Retrieval	1024	8192	CPU/GPU	Text embeddings for RAG

Capabilities:

✅ Document embedding for retrieval
✅ Query embedding
✅ Multi-lingual support
✅ Long context (8192 tokens)

Storage:

Vector database: PostgreSQL with pgvector extension
Indexed documents: Chat messages, tasks, meetings, governance docs

Configuration:

Model: BAAI/bge-m3
Device: cpu (can use GPU if available)
HuggingFace cache: /root/.cache/huggingface

4. Audio Models

Status: ❌ Not installed yet

Planned:

Whisper (speech-to-text)
TTS models (text-to-speech)
Audio classification

🗄️ Vector Databases

1. Qdrant (Image Embeddings)

Service: qdrant
Port: 6333 (HTTP), 6334 (gRPC)
Status: ✅ Active

Collections:

Collection	Vectors	Dimension	Distance	Purpose
daarion_images	Variable	768	Cosine	Image search (text→image, image→image)

Storage: Docker volume qdrant-data

API:

# Health check
curl http://localhost:6333/healthz

# List collections
curl http://localhost:6333/collections

# Collection info
curl http://localhost:6333/collections/daarion_images

2. PostgreSQL + pgvector (Text Embeddings)

Service: dagi-postgres
Port: 5432
Status: ✅ Active

Databases:

Database	Extension	Purpose
daarion_memory	-	Agent memory, context
daarion_city	pgvector	RAG document storage (1024-dim)

Storage: Docker volume postgres-data

3. Neo4j (Graph Memory)

Service: neo4j
Port: 7687 (Bolt), 7474 (HTTP)
Status: ✅ Active (optional)

Purpose:

Knowledge graph for entities
Agent relationships
DAO structure mapping

Storage: Docker volume (if configured)

🛠️ AI Services

1. DAGI Router (9102)

Purpose: Main routing engine for AI requests
LLM Integration:

Ollama (qwen3:8b)
DeepSeek (optional, API key required)
OpenAI (optional, API key required)

Providers:

LLM Provider (Ollama, DeepSeek, OpenAI)
Vision Encoder Provider (OpenCLIP)
DevTools Provider
CrewAI Provider
Vision RAG Provider (image search)

2. RAG Service (9500)

Purpose: Document retrieval and Q&A
Models:

Embeddings: BAAI/bge-m3 (1024-dim)
LLM: via DAGI Router (qwen3:8b)

Capabilities:

Document ingestion (chat, tasks, meetings, governance, RWA, oracle)
Vector search (pgvector)
Q&A generation
Context ranking

3. Vision Encoder (8001)

Purpose: Text/Image embeddings for multimodal RAG
Models:

OpenCLIP ViT-L/14 (768-dim)

Capabilities:

Text embeddings
Image embeddings
Image search (text-to-image, image-to-image)

4. Parser Service (9400)

Purpose: Document parsing and processing
Capabilities:

PDF parsing
Image extraction
OCR (via Crawl4AI)
Q&A generation

Integration:

Crawl4AI for web content
Vision Encoder for image analysis (planned)

5. Memory Service (8000)

Purpose: Agent memory and context management
Storage:

PostgreSQL (daarion_memory)
Redis (short-term cache, optional)
Neo4j (graph memory, optional)

6. CrewAI Orchestrator (9010)

Purpose: Multi-agent workflow execution
LLM: via DAGI Router (qwen3:8b)

Workflows:

microDAO onboarding
Code review
Proposal review
Task decomposition

7. DevTools Backend (8008)

Purpose: Development tool execution
Tools:

File operations (read/write)
Test execution
Notebook execution
Git operations (planned)

8. Bot Gateway (9300)

Purpose: Telegram/Discord bot integration
Bots:

DAARWIZZ (Telegram)
Helion (Telegram, Energy Union)

9. RBAC Service (9200)

Purpose: Role-based access control
Storage: SQLite (rbac.db)

📊 GPU Memory Allocation (Estimated)

Total VRAM: 24 GB

Service	Model	VRAM Usage	Status
Vision Encoder	OpenCLIP ViT-L/14	~4 GB	Always loaded
Ollama	qwen3:8b	~6 GB	Loaded on demand
Available	-	~14 GB	For other models

Note:

Ollama and Vision Encoder can run simultaneously (~10 GB total)
Remaining 14 GB available for additional models (audio, larger LLMs, etc.)

🔄 Model Loading Strategy

Vision Encoder (Always-On)

Preloaded: Yes (on service startup)
Reason: Fast inference for image search
Unload: Never (unless service restart)

Ollama qwen3:8b (On-Demand)

Preloaded: No
Load Time: 2-3 seconds (first request)
Keep Alive: 5 minutes (default)
Unload: After idle timeout

Future Models (Planned)

Whisper: Load on-demand for audio transcription
TTS: Load on-demand for speech synthesis
Larger LLMs: Load on-demand (if VRAM available)

📈 Performance Benchmarks

LLM Inference (qwen3:8b)

Tokens/sec: ~50-80 tokens/sec (GPU)
Latency: 100-200ms (first token)
Context: 32K tokens
Batch size: 1 (default)

Vision Inference (ViT-L/14)

Text embedding: 10-20ms (GPU)
Image embedding: 30-50ms (GPU)
Throughput: 50-100 images/sec (batch)

RAG Search (BAAI/bge-m3)

Query embedding: 50-100ms (CPU)
Vector search: 5-10ms (pgvector)
Total latency: 60-120ms

🔧 Model Management

Ollama Models

List installed models:

curl http://localhost:11434/api/tags

Pull new model:

ollama pull llama2:7b
ollama pull mistral:7b

Remove model:

ollama rm qwen3:8b

Check model info:

ollama show qwen3:8b

Vision Encoder Models

Change model (in docker-compose.yml):

environment:
  - MODEL_NAME=ViT-B-32  # Smaller, faster
  - MODEL_PRETRAINED=openai

Available models:

ViT-B-32 (512-dim, 2 GB VRAM)
ViT-L-14 (768-dim, 4 GB VRAM) ← Current
ViT-L-14@336 (768-dim, 6 GB VRAM, higher resolution)
ViT-H-14 (1024-dim, 8 GB VRAM, highest quality)

📋 Complete Service List (17 Services)

#	Service	Port	GPU	Models/Tools	Status
1	DAGI Router	9102	❌	Routing engine	✅
2	Bot Gateway	9300	❌	Telegram bots	✅
3	DevTools	8008	❌	File ops, tests	✅
4	CrewAI	9010	❌	Multi-agent	✅
5	RBAC	9200	❌	Access control	✅
6	RAG Service	9500	❌	BAAI/bge-m3	✅
7	Memory Service	8000	❌	Context mgmt	✅
8	Parser Service	9400	❌	PDF, OCR	✅
9	Vision Encoder	8001	✅	OpenCLIP ViT-L/14	✅
10	PostgreSQL	5432	❌	pgvector	✅
11	Redis	6379	❌	Cache	✅
12	Neo4j	7687	❌	Graph DB	✅
13	Qdrant	6333	❌	Vector DB	✅
14	Grafana	3000	❌	Dashboards	✅
15	Prometheus	9090	❌	Metrics	✅
16	Neo4j Exporter	9091	❌	Metrics	✅
17	Ollama	11434	✅	qwen3:8b	✅

GPU Services: 2 (Vision Encoder, Ollama)
Total VRAM Usage: ~10 GB (concurrent)

🚀 Deployment Checklist

Pre-Deployment (Local)

Code reviewed and tested
Documentation updated (WARP.md, INFRASTRUCTURE.md)
Jupyter Notebook updated
All tests passing
Git committed and pushed

Deployment (Server)

# 1. SSH to server
ssh root@144.76.224.179

# 2. Pull latest code
cd /opt/microdao-daarion
git pull origin main

# 3. Check GPU
nvidia-smi

# 4. Build new services
docker-compose build vision-encoder

# 5. Start all services
docker-compose up -d

# 6. Verify health
docker-compose ps
curl http://localhost:8001/health  # Vision Encoder
curl http://localhost:6333/healthz # Qdrant
curl http://localhost:9102/health  # Router

# 7. Run smoke tests
./smoke.sh
./test-vision-encoder.sh

# 8. Check logs
docker-compose logs -f vision-encoder
docker-compose logs -f router

# 9. Monitor GPU
watch -n 1 nvidia-smi

📖 Documentation Index

WARP.md — Developer guide (quick start for Warp AI)
INFRASTRUCTURE.md — Server, services, deployment
VISION-ENCODER-STATUS.md — Vision Encoder status
VISION-RAG-IMPLEMENTATION.md — Vision RAG complete implementation
docs/cursor/vision_encoder_deployment_task.md — Deployment task
docs/infrastructure_quick_ref.ipynb — Jupyter quick reference

🎯 Next Steps

Phase 1: Audio Integration

Install Whisper (speech-to-text)
Install TTS model (text-to-speech)
Integrate with Telegram voice messages
Audio RAG (transcription + search)

Phase 2: Larger LLMs

Install Mistral 7B (better reasoning)
Install Llama 2 70B (if enough VRAM via quantization)
Multi-model routing (task-specific models)

Phase 3: Advanced Vision

Image captioning (BLIP-2)
Zero-shot classification
Video understanding (frame extraction + CLIP)

Phase 4: Optimization

Model quantization (reduce VRAM)
Batch inference (increase throughput)
Model caching (Redis)
GPU sharing (multiple models concurrently)

Last Updated: 2025-01-17
Maintained by: Ivan Tytar & DAARION Team
Status: ✅ Production Ready (17 services, 3 AI models)

13 KiB Raw Blame History

🖥️ System Inventory — DAARION & MicroDAO

🖥️ Hardware Specifications

Production Server (144.76.224.179)

GPU Configuration

CPU & RAM (Typical GEX44)

🤖 Installed AI Models

1. LLM Models (Language Models)

Ollama (Local)

2. Vision Models (Multimodal)

OpenCLIP (Vision Encoder Service)

3. Embedding Models (Text)

BAAI/bge-m3 (RAG Service)

4. Audio Models

🗄️ Vector Databases

1. Qdrant (Image Embeddings)

2. PostgreSQL + pgvector (Text Embeddings)

3. Neo4j (Graph Memory)

🛠️ AI Services

1. DAGI Router (9102)

2. RAG Service (9500)

3. Vision Encoder (8001)

4. Parser Service (9400)

5. Memory Service (8000)

6. CrewAI Orchestrator (9010)

7. DevTools Backend (8008)

8. Bot Gateway (9300)

9. RBAC Service (9200)

📊 GPU Memory Allocation (Estimated)

🔄 Model Loading Strategy

Vision Encoder (Always-On)

Ollama qwen3:8b (On-Demand)

Future Models (Planned)

📈 Performance Benchmarks

LLM Inference (qwen3:8b)

Vision Inference (ViT-L/14)

RAG Search (BAAI/bge-m3)

🔧 Model Management

Ollama Models

Vision Encoder Models

📋 Complete Service List (17 Services)

🚀 Deployment Checklist

Pre-Deployment (Local)

Deployment (Server)

📖 Documentation Index

🎯 Next Steps

Phase 1: Audio Integration

Phase 2: Larger LLMs

Phase 3: Advanced Vision

Phase 4: Optimization

13 KiB

Raw Blame History