Ollama Local LLM Container Orchestration — รัน LLM บนเครื่องด้วย Docker Kubernetes | SiamCafe Blog

Ollama Container Orchestration

Ollama Local LLM Llama Mistral Gemma Docker Kubernetes Container GPU Scheduling Auto-scaling REST API Privacy Open Source NVIDIA

Docker Deployment

# === Ollama Docker Setup ===

# Basic Docker Run
# docker run -d \
# --gpus all \
# -v ollama:/root/.ollama \
# -p 11434:11434 \
# --name ollama \
# ollama/ollama

# Pull and Run Model
# docker exec -it ollama ollama pull llama3:8b
# docker exec -it ollama ollama pull mistral:7b
# docker exec -it ollama ollama pull codellama:13b

# Docker Compose with Multiple Models
# version: '3.8'
# services:
# ollama:
# image: ollama/ollama:latest
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: all
# capabilities: [gpu]
# volumes:
# - ollama_data:/root/.ollama
# ports:
# - "11434:11434"
# restart: always
# healthcheck:
# test: ["CMD", "curl", "-f", "http://localhost:11434/"]
# interval: 30s
# timeout: 10s
# retries: 3
#
# ollama-webui:
# image: ghcr.io/open-webui/open-webui:main
# ports:
# - "3000:8080"
# environment:
# - OLLAMA_BASE_URL=http://ollama:11434
# depends_on:
# - ollama
# volumes:
# - webui_data:/app/backend/data
#
# nginx:
# image: nginx:alpine
# ports:
# - "443:443"
# volumes:
# - ./nginx.conf:/etc/nginx/nginx.conf
# - ./certs:/etc/nginx/certs
# depends_on:
# - ollama
#
# volumes:
# ollama_data:
# webui_data:

# API Usage
# curl http://localhost:11434/api/generate -d '{
# "model": "llama3:8b",
# "prompt": "Explain Docker in 3 sentences",
# "stream": false
# }'

from dataclasses import dataclass
from typing import List

@dataclass
class OllamaModel:
 name: str
 size_gb: float
 params: str
 vram_gb: float
 speed_tokens_s: float
 use_case: str

models = [
 OllamaModel("llama3:8b", 4.7, "8B", 6, 45, "General Chat, RAG"),
 OllamaModel("mistral:7b", 4.1, "7B", 5, 50, "Fast General Purpose"),
 OllamaModel("codellama:13b", 7.4, "13B", 10, 30, "Code Generation"),
 OllamaModel("gemma2:9b", 5.4, "9B", 7, 40, "Google Model, Multilingual"),
 OllamaModel("phi3:mini", 2.3, "3.8B", 3, 65, "Small Fast Efficient"),
 OllamaModel("llama3:70b-q4", 40, "70B", 48, 10, "High Quality Large"),
]

print("=== Ollama Models ===")
for m in models:
 print(f" [{m.name}] {m.params} | Size: {m.size_gb}GB | VRAM: {m.vram_gb}GB")
 print(f" Speed: {m.speed_tokens_s} tok/s | Use: {m.use_case}")

Kubernetes Deployment

# === Kubernetes GPU Deployment ===

# Install NVIDIA GPU Operator
# helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
# helm install gpu-operator nvidia/gpu-operator \
# --namespace gpu-operator --create-namespace

# Ollama Deployment
# apiVersion: apps/v1
# kind: Deployment
# metadata:
# name: ollama
# namespace: llm
# spec:
# replicas: 2
# selector:
# matchLabels:
# app: ollama
# template:
# metadata:
# labels:
# app: ollama
# spec:
# containers:
# - name: ollama
# image: ollama/ollama:latest
# ports:
# - containerPort: 11434
# resources:
# requests:
# memory: "8Gi"
# cpu: "2"
# nvidia.com/gpu: "1"
# limits:
# memory: "16Gi"
# cpu: "4"
# nvidia.com/gpu: "1"
# volumeMounts:
# - name: ollama-data
# mountPath: /root/.ollama
# readinessProbe:
# httpGet:
# path: /
# port: 11434
# initialDelaySeconds: 30
# periodSeconds: 10
# livenessProbe:
# httpGet:
# path: /
# port: 11434
# initialDelaySeconds: 60
# periodSeconds: 30
# volumes:
# - name: ollama-data
# persistentVolumeClaim:
# claimName: ollama-pvc
#
# ---
# apiVersion: v1
# kind: Service
# metadata:
# name: ollama-svc
# namespace: llm
# spec:
# selector:
# app: ollama
# ports:
# - port: 11434
# targetPort: 11434
# type: ClusterIP
#
# ---
# apiVersion: autoscaling/v2
# kind: HorizontalPodAutoscaler
# metadata:
# name: ollama-hpa
# namespace: llm
# spec:
# scaleTargetRef:
# apiVersion: apps/v1
# kind: Deployment
# name: ollama
# minReplicas: 1
# maxReplicas: 4
# metrics:
# - type: Resource
# resource:
# name: cpu
# target:
# type: Utilization
# averageUtilization: 70

@dataclass
class K8sNode:
 name: str
 gpu: str
 gpu_count: int
 vram_gb: int
 pods_running: int
 gpu_utilization: float

nodes = [
 K8sNode("gpu-node-01", "A100", 2, 80, 3, 72),
 K8sNode("gpu-node-02", "A100", 2, 80, 2, 55),
 K8sNode("gpu-node-03", "RTX 4090", 4, 24, 4, 85),
]

print("\n=== Kubernetes GPU Nodes ===")
for n in nodes:
 print(f" [{n.name}] {n.gpu} x{n.gpu_count} ({n.vram_gb}GB each)")
 print(f" Pods: {n.pods_running} | GPU Util: {n.gpu_utilization}%")

Monitoring และ Optimization

# === Monitoring & Performance ===

# Prometheus Metrics
# ollama_request_duration_seconds
# ollama_tokens_generated_total
# ollama_model_load_duration_seconds
# nvidia_gpu_utilization_percentage
# nvidia_gpu_memory_used_bytes

# Python Client
# import requests
#
# class OllamaClient:
# def __init__(self, base_url="http://localhost:11434"):
# self.base_url = base_url
#
# def generate(self, model, prompt, stream=False):
# response = requests.post(f"{self.base_url}/api/generate", json={
# "model": model, "prompt": prompt, "stream": stream,
# })
# return response.json()
#
# def chat(self, model, messages, stream=False):
# response = requests.post(f"{self.base_url}/api/chat", json={
# "model": model, "messages": messages, "stream": stream,
# })
# return response.json()
#
# def list_models(self):
# return requests.get(f"{self.base_url}/api/tags").json()
#
# def pull(self, model):
# return requests.post(f"{self.base_url}/api/pull",
# json={"name": model})

optimization = {
 "Quantization": "ใช้ Q4_K_M ลด VRAM 50-60% คุณภาพลดเล็กน้อย",
 "Context Length": "ลด Context Length ถ้าไม่ต้องการยาว ลด VRAM",
 "Batch Size": "เพิ่ม Batch Size ถ้ามี VRAM เพียงพอ เพิ่ม Throughput",
 "Model Caching": "Keep Model ใน Memory ไม่ต้อง Load ใหม่ทุกครั้ง",
 "GPU Affinity": "Pin Pod กับ GPU เฉพาะ ลด Scheduling Overhead",
 "Preload Models": "Preload Models ตอน Container Start",
}

print("Performance Optimization:")
for tip, desc in optimization.items():
 print(f" [{tip}]: {desc}")

# Cost Comparison
costs = {
 "Ollama Self-hosted (RTX 4090)": {"monthly": "$50 (electricity)", "per_1k": "$0.001"},
 "Ollama Cloud GPU (A100)": {"monthly": "$800-1500", "per_1k": "$0.01"},
 "OpenAI GPT-4o-mini": {"monthly": "Pay-per-use", "per_1k": "$0.15"},
 "OpenAI GPT-4o": {"monthly": "Pay-per-use", "per_1k": "$2.50"},
 "Anthropic Claude Sonnet": {"monthly": "Pay-per-use", "per_1k": "$3.00"},
}

print(f"\n\nCost Comparison:")
for provider, cost in costs.items():
 print(f" [{provider}]")
 print(f" Monthly: {cost['monthly']} | Per 1K tokens: {cost['per_1k']}")

เคล็ดลับ

• Quantize: ใช้ Q4 Quantization ลด VRAM คุณภาพยังดี
• Health Check: ตั้ง Health Check ทุก Container ตรวจ API Response
• GPU Operator: ใช้ NVIDIA GPU Operator บน K8s จัดการ GPU อัตโนมัติ
• Preload: Preload Model ที่ใช้บ่อยตอน Container Start
• Monitor: ดู GPU Utilization และ VRAM ตลอด ป้องกัน OOM

Ollama คืออะไร

รัน LLM Local Llama Mistral Gemma ติดตั้งง่าย REST API GPU NVIDIA Apple Silicon ฟรี Open Source Privacy