Complete Guide to Local LLMs + OpenClaw

Target keywords: local LLM OpenClaw, Ollama OpenClaw, self-hosted AI, local AI assistant

Introduction

Concerned about privacy? Don’t want to depend on OpenAI? This guide shows you how to run large language models locally and build a fully private AI assistant with OpenClaw.

What you will learn:

How to choose a local LLM
Ollama installation and configuration
OpenClaw integration
Performance optimization

Estimated time: 45 minutes
Difficulty: Intermediate

Why Use a Local LLM?

Advantages

Category	Cloud API	Local LLM
Privacy	Data is sent to a third party	Fully local, zero external leakage
Cost	Pay per token	One-time hardware investment
Latency	Depends on the network	Millisecond-level response
Control	Limited	Full control
Offline use	Requires network access	Runs fully offline

Good Fit Scenarios

✅ Privacy-sensitive applications

Medical consultation
Legal document analysis
Internal enterprise knowledge bases

✅ Cost-sensitive workloads

High-frequency calls
Large-text processing
Long-running services

✅ Offline environments

Internal network deployment
Edge devices
Regions with unstable connectivity

Hardware Requirements

Model Size vs VRAM Requirements

Model	Parameters	VRAM Requirement	Suitable Hardware
TinyLlama	1.1B	2GB	Laptop CPU
Llama 3.1	8B	6GB	GTX 1060
Llama 3.1	70B	40GB+	RTX 4090 / A100
Mixtral 8x7B	47B	28GB	RTX 3090
Qwen 2.5	72B	40GB+	Multi-GPU / cloud instance

Recommended Configurations

Entry level (7B-8B models):

GPU: GTX 1060 6GB / RTX 3060
RAM: 16GB
Storage: 50GB SSD

Advanced (13B-30B models):

GPU: RTX 3090 24GB / RTX 4090
RAM: 32GB
Storage: 100GB SSD

Professional (70B+ models):

GPU: 2x RTX 4090 / A100 80GB
RAM: 64GB+
Storage: 200GB NVMe

Step 1: Install Ollama

macOS

# Use Homebrew
brew install ollama

# Start the service
brew services start ollama

Linux

# One-command installation
curl -fsSL https://ollama.com/install.sh | sh

# Start the service
sudo systemctl start ollama
sudo systemctl enable ollama

Windows

# Download the installer
# https://ollama.com/download/windows

# Or use WSL2 (recommended)
wsl --install
# Then run the Linux installation command in WSL

Docker (Recommended for Servers)

# Run Ollama
docker run -d \
  -v ollama:/root/.ollama \
  -p 11434:11434 \
  --name ollama \
  ollama/ollama

Step 2: Download Models

View Available Models

# List official models
ollama list

# Search models
ollama search llama

Download Recommended Models

# Llama 3.1 (8B) - Balanced performance and quality
ollama pull llama3.1

# Llama 3.1 (70B) - Best quality (requires a lot of VRAM)
ollama pull llama3.1:70b

# Mixtral - Excellent multilingual capability
ollama pull mixtral

# Qwen 2.5 - Excellent Chinese performance
ollama pull qwen2.5

# Phi-4 - Small Microsoft model, fast
ollama pull phi4

Verify the Installation

# Test the model
ollama run llama3.1

# Enter a test message
>>> 你好，请介绍一下自己

# Exit
/bye

Step 3: Configure OpenClaw

3.1 Install the Ollama Skill

openclaw skill install ollama

3.2 Configure the Connection

Edit the OpenClaw config:

openclaw config edit

Add the Ollama configuration:

# ~/.openclaw/config.yaml
models:
  local-llama:
    provider: ollama
    base_url: "http://localhost:11434"  # Ollama default port
    model: "llama3.1"
    temperature: 0.7
    
  local-mixtral:
    provider: ollama
    base_url: "http://localhost:11434"
    model: "mixtral"
    temperature: 0.8

# Set the default model
default_model: local-llama

3.3 Create a Local Agent

openclaw agent create local-assistant

Configuration:

# ~/.openclaw/agents/local-assistant.yaml
agent:
  name: "Local AI Assistant"
  description: "Fully private AI assistant"
  
  model: local-llama  # Use the model configured above
  
  system_prompt: |
    You are a helpful AI assistant running locally.
    You respect user privacy and never share data externally.
    
  memory:
    enabled: true
    type: local  # Store locally, not in the cloud

3.4 Test the Connection

# Test the local model
openclaw chat local-assistant "Hello, are you running locally?"

# Expected reply:
# "Yes, I'm running entirely on your local machine..."

Step 4: Advanced Configuration

4.1 Model Quantization (Save VRAM)

Quantization reduces model size so larger models can run on smaller GPUs:

# Download the quantized version (4-bit)
ollama pull llama3.1:70b-q4_0

# Comparison:
# 70b (FP16): ~140GB
# 70b-q4_0: ~40GB

Quantization levels:

q4_0: 4-bit, smallest, fastest, with slightly lower quality
q5_0: 5-bit, balanced choice
q8_0: 8-bit, close to original quality

4.2 Multi-Model Switching

# Switch models based on the task
routes:
  chat:
    - pattern: "*"
      agent: local-assistant
      model: local-llama  # Default
      
    - pattern: "*code*"
      agent: local-assistant
      model: local-code  # Model for code
      
    - pattern: "*translate*"
      agent: local-assistant
      model: local-mixtral  # Multilingual model

4.3 Context Length Tuning

models:
  local-llama:
    provider: ollama
    model: "llama3.1"
    context_window: 8192  # Default 4096
    max_tokens: 2048

Step 5: Performance Optimization

5.1 GPU Acceleration

NVIDIA GPU:

# Make sure CUDA is installed
docker run -d \
  --gpus all \
  -v ollama:/root/.ollama \
  -p 11434:11434 \
  ollama/ollama

AMD GPU:

# Use ROCm
docker run -d \
  --device /dev/kfd \
  --device /dev/dri \
  -v ollama:/root/.ollama \
  -p 11434:11434 \
  ollama/ollama:rocm

Apple Silicon:

# Metal is enabled automatically
ollama serve

5.2 CPU Optimization

If you do not have a GPU, optimize CPU inference:

# Use all CPU cores
export OLLAMA_NUM_PARALLEL=4

# Enable memory mapping (reduce RAM usage)
export OLLAMA_USE_MMAP=true

ollama serve

5.3 Concurrent Processing

# OpenClaw configuration
models:
  local-llama:
    provider: ollama
    model: "llama3.1"
    max_concurrent: 4  # Maximum concurrent requests
    timeout: 60  # Timeout in seconds

5.4 Caching Strategy

agent:
  cache:
    enabled: true
    ttl: 3600  # Cache for 1 hour
    similar_responses: true  # Reuse answers for similar questions

Step 6: Production Deployment

6.1 Internal Network Deployment

# Listen only on the internal network
server:
  host: 192.168.1.100  # Internal network IP
  port: 8080

models:
  local-llama:
    provider: ollama
    base_url: "http://192.168.1.100:11434"  # Same machine or another machine on the internal network

6.2 Multi-Machine Deployment

Architecture:

[OpenClaw Server] ←→ [Ollama Server 1]
                ←→ [Ollama Server 2]
                ←→ [Ollama Server 3]

Configure load balancing:

models:
  local-llama:
    provider: ollama
    base_urls:
      - "http://ollama-1:11434"
      - "http://ollama-2:11434"
      - "http://ollama-3:11434"
    load_balance: round_robin

6.3 Model Preloading

Avoid latency on the first request:

# Load the model automatically at startup
ollama run llama3.1 &

# Or set it in the configuration
export OLLAMA_KEEP_ALIVE=24h  # Keep the model loaded for 24 hours

Step 7: Troubleshooting

Issue 1: Out of VRAM (OOM)

Symptom: CUDA out of memory

Fix:

# 1. Use a smaller model
ollama pull llama3.1  # Use 8B instead of 70B

# 2. Use a quantized version
ollama pull llama3.1:q4_0

# 3. Reduce the context length
# Set `context_window: 2048` in the configuration

Issue 2: Responses are too slow

Symptom: A single token takes several seconds to generate

Fix:

# 1. Confirm the GPU is being used
nvidia-smi  # Check GPU utilization

# 2. Use a faster quantization level
ollama pull llama3.1:q4_0  # 2-3x faster than q8_0

# 3. Enable streaming responses
# Add `stream: true` to the agent configuration

Issue 3: Model download failed

Symptom: The pull command hangs or fails

Fix:

# 1. Check the network
ping ollama.com

# 2. Use a mirror
export OLLAMA_REGISTRY="https://ollama.mirror.example.com"

# 3. Download manually
# Download the GGUF file from HuggingFace
# Then run: `ollama create mymodel -f Modelfile`

Issue 4: Garbled Chinese output

Symptom: Chinese text is displayed as question marks or corrupted characters

Fix:

# Use a model optimized for Chinese
model: qwen2.5  # OR
model: llama3.1-chinese  # Community fine-tuned Chinese version

# Set the system prompt
system_prompt: |
  请用中文回答所有问题。
  保持回答简洁明了。

Model Recommendations

Choose by Use Case

Scenario	Recommended Model	Reason
General conversation	Llama 3.1 8B	Balanced performance and quality
Code generation	CodeLlama 34B	Strong code understanding
Chinese tasks	Qwen 2.5 72B	Optimized for Chinese
Long documents	Mixtral 8x7B	32K context
Edge devices	Phi-4	Small model, fast
Creative writing	Llama 3.1 70B	High-quality generation

Performance Comparison

Model	VRAM	Speed (tok/s)	Quality
Llama 3.1 8B	6GB	50+	⭐⭐⭐⭐
Llama 3.1 70B	40GB+	10+	⭐⭐⭐⭐⭐
Mixtral 8x7B	28GB	15+	⭐⭐⭐⭐⭐
Qwen 2.5 72B	40GB+	12+	⭐⭐⭐⭐⭐
Phi-4	4GB	80+	⭐⭐⭐

Security Best Practices

1. Network Isolation

# Allow only internal network access
server:
  host: 127.0.0.1  # Or an internal network IP
  
# Firewall rule
# sudo ufw allow from 192.168.1.0/24 to any port 11434

2. Access Control

# API key validation
security:
  api_key_required: true
  allowed_ips:
    - "192.168.1.0/24"
    - "10.0.0.0/8"

3. Data Protection

agent:
  memory:
    encryption: true  # Encrypt stored conversation history
    retention: 7d     # Automatically delete after 7 days

Cost Analysis

Self-Hosted vs Cloud API (Monthly)

Scenario: 1 million tokens per day

Option	Hardware Cost	Electricity	Total	Equivalent API Cost
Self-hosted RTX 4090	$100/month (amortized)	$30	$130	~$1,500 (GPT-4)
Cloud server (A100)	$500/month	-	$500	~$1,500
Laptop CPU	$0	$10	$10	~$300 (GPT-3.5)

ROI: Self-hosting typically pays off after 3-6 months

Next Steps

Resources

Get more tutorials for free:

[Email subscription form]

Last updated: March 2026

Local LLM Guide

Complete Guide to Local LLMs + OpenClaw

Introduction

Why Use a Local LLM?

Advantages

Good Fit Scenarios

Hardware Requirements

Model Size vs VRAM Requirements

Recommended Configurations

Step 1: Install Ollama

macOS

Linux

Windows

Docker (Recommended for Servers)

Step 2: Download Models

View Available Models

Download Recommended Models

Verify the Installation

Step 3: Configure OpenClaw

3.1 Install the Ollama Skill

3.2 Configure the Connection

3.3 Create a Local Agent

3.4 Test the Connection

Step 4: Advanced Configuration

4.1 Model Quantization (Save VRAM)

4.2 Multi-Model Switching

4.3 Context Length Tuning

Step 5: Performance Optimization

5.1 GPU Acceleration

5.2 CPU Optimization

5.3 Concurrent Processing

5.4 Caching Strategy

Step 6: Production Deployment

6.1 Internal Network Deployment

6.2 Multi-Machine Deployment

6.3 Model Preloading

Step 7: Troubleshooting

Issue 1: Out of VRAM (OOM)

Issue 2: Responses are too slow

Issue 3: Model download failed

Issue 4: Garbled Chinese output

Model Recommendations

Choose by Use Case

Performance Comparison

Security Best Practices

1. Network Isolation

2. Access Control

3. Data Protection

Cost Analysis

Self-Hosted vs Cloud API (Monthly)

Next Steps

Resources