ok... ok... I'll add it to git...

stacks/ollama.yml

@@ -0,0 +1,56 @@
+version: '3.8'
+
+# Ollama - GPU-Accelerated ML Model Serving
+# Phase 1: Foundation Setup
+# Ports: 11434 (API)
+# GPU: YES - Requires NVIDIA Container Toolkit
+# Storage: SSD or HDD for models (models are 2-15GB each)
+
+services:
+  ollama:
+    image: ollama/ollama:latest
+    container_name: ollama
+    restart: unless-stopped
+    ports:
+      - "11434:11434"  # Ollama API endpoint
+    volumes:
+      # Model storage - choose based on available space:
+      # SSD (faster load times): /home/jpmschweitzer/docker-data/ollama/models
+      # HDD (more space): /mnt/media/ollama/models
+      - /home/jpmschweitzer/docker-data/ollama/models:/root/.ollama
+    environment:
+      - TZ=Europe/Amsterdam
+      - NVIDIA_VISIBLE_DEVICES=all
+      - NVIDIA_DRIVER_CAPABILITIES=all
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+# GPU Requirements:
+# - RTX 2080 Ti (11GB VRAM)
+# - Suitable for 3B-13B parameter models
+# - NVIDIA Container Toolkit must be installed
+#
+# After Deployment:
+# 1. Verify GPU access: docker exec ollama nvidia-smi
+# 2. Pull a model: docker exec ollama ollama pull llama3.2:3b
+# 3. List models: docker exec ollama ollama list
+# 4. Test inference: docker exec ollama ollama run llama3.2:3b "Hello"
+# 5. Monitor GPU during inference: watch -n 1 nvidia-smi
+#
+# Recommended Models for RTX 2080 Ti (11GB VRAM):
+# - llama3.2:3b (2GB) - Fast, general purpose
+# - mistral:7b (4GB) - High quality, coding
+# - codellama:7b (4GB) - Code-specialized
+# - phi3:mini (2GB) - Fast reasoning
+#
+# API Usage:
+# curl http://localhost:11434/api/generate -d '{
+#   "model": "llama3.2:3b",
+#   "prompt": "Why is the sky blue?",
+#   "stream": false
+# }'