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OpenClawInstall
🖥️ New Service

Run OpenClaw on Local Models
Zero API costs. Total privacy. Your hardware.

Tired of $50-200/month in API bills? Want your AI assistant to work offline, keep your data local, and never hit rate limits? I'll set up OpenClaw with Ollama and the best local model for your hardware — in a single live session.

See Local Packages →Check My Hardware

Why Run Local?

Cloud APIs are powerful — but they come with costs, privacy tradeoffs, and dependencies. Here's what changes when you go local.

💸

API costs adding up

Claude and GPT-4 API bills can hit $50-200+/month for heavy use. Local models cost nothing after hardware.

🔒

Privacy concerns

Your prompts, files, and personal data never leave your machine. No cloud, no logs, no third parties.

🌐

Works offline

No internet? No problem. Your AI assistant works on a plane, in the mountains, during outages.

⚡

Speed & latency

Local inference means no waiting for API rate limits, no server congestion, no 429 errors.

Local vs. Cloud: Honest Comparison

No hype. Here's the real tradeoff.

🖥️ Local
☁️ Cloud API
Monthly AI cost
$0
$50-200+
Data privacy
100% local
Sent to cloud servers
Works offline
✓
✗
Response speed
Instant (no network)
Depends on API load
Rate limits
None
Yes (429 errors)
Quality (simple tasks)
★★★★☆
★★★★★
Quality (complex tasks)
★★★☆☆
★★★★★
Best for
Daily use, privacy, budget
Complex reasoning, coding

Best approach? Use both. Local Pro includes hybrid routing — local for daily tasks, cloud for complex ones.

Models I Set Up

Not every local model works well with OpenClaw. I've tested dozens — these are the ones that actually deliver.

Qwen 2.5

Recommended

Alibaba / Ollama — 7B–72B parameters

Best all-around for OpenClaw — great tool calling, multilingual

LLaMA 3.1

Meta / Ollama — 8B–70B parameters

Strong reasoning, massive community, well-documented

Mistral / Mixtral

Mistral AI / Ollama — 7B–8x7B parameters

Fast inference, good at structured output and function calling

DeepSeek-V3

DeepSeek / Ollama — 7B–67B parameters

Excellent coding, strong reasoning at smaller sizes

Phi-3

Microsoft / Ollama — 3.8B–14B parameters

Surprisingly capable at tiny sizes — great for older hardware

New models drop constantly. During your session, I'll teach you how to swap models yourself — one command in Ollama.

Will My Hardware Work?

Short answer: probably. Here's what to expect at each level.

Entry Level

GPU: 8GB VRAM (RTX 3060, M1 Mac)
Models: 7B models (Qwen 2.5 7B, LLaMA 3.1 8B, Phi-3)
Speed: ~15-30 tokens/sec
Experience: Good for basic tasks, chat, simple automation

Sweet Spot

Recommended
GPU: 16-24GB VRAM (RTX 4070 Ti, M2 Pro/Max)
Models: 7B-14B models with room to spare
Speed: ~30-60 tokens/sec
Experience: Smooth for daily use, tool calling, complex tasks

Power User

GPU: 32GB+ VRAM (RTX 4090, M3 Max, dual GPU)
Models: Up to 70B models quantized
Speed: ~20-50 tokens/sec on large models
Experience: Near cloud-quality responses, fast inference

CPU Only

GPU: No GPU — 16GB+ RAM
Models: 3.8B-7B models (Phi-3, small Qwen)
Speed: ~3-8 tokens/sec
Experience: Usable but slow. Good for light tasks and experimenting.

Not sure where you fall? Send me your specs and I'll tell you exactly what to expect.

Local Setup Packages

Every setup includes Ollama installation, model optimization for your hardware, and OpenClaw gateway configuration. Pick your level.

Local Basic

$14945-minute session

Get one local model running with OpenClaw.

  • ✓Ollama installation & configuration
  • ✓One local model optimized for your hardware
  • ✓OpenClaw gateway configured for local inference
  • ✓One messaging channel (Telegram or Discord)
  • ✓Performance tuning for your GPU/CPU
  • ✓Post-session setup notes

You have a decent GPU and want to stop paying API bills.

Get Local Basic — $149
Best Value

Local Pro

$2494-hour session

Full local setup with hybrid cloud fallback.

  • ✓Everything in Local Basic
  • ✓Multiple model setup (fast model + smart model)
  • ✓Hybrid routing: local for daily tasks, cloud API for complex ones
  • ✓Multi-channel messaging setup
  • ✓Custom skills & personality tuning
  • ✓Tool calling optimization for local models
  • ✓1 week follow-up support

You want the best of both worlds — local speed & privacy with cloud brains on demand.

Get Local Pro — $249

Local Enterprise

$4492.5-hour session

Multi-machine or team local LLM deployment.

  • ✓Everything in Local Pro
  • ✓Multi-machine setup (serve from a home server, use anywhere)
  • ✓Team configuration (multiple users, one inference server)
  • ✓Advanced model fine-tuning guidance
  • ✓Custom API endpoint configuration
  • ✓Network security hardening
  • ✓2 weeks follow-up support
  • ✓Configuration backup & documentation

You're running a home lab or want a team-wide local AI setup.

Get Enterprise — $449

💰 Already have a cloud setup? Add local as a supplement to any existing package — ask about add-on pricing.

Why Not Just Do It Yourself?

You absolutely can. Here's what you're signing up for.

❌

Tool calling is broken with most models

The #1 complaint on r/LocalLLaMA. Most local models don't reliably call tools (calendar, email, web search). You'll spend hours debugging why your AI can't actually *do* anything.

❌

Model selection is a maze

Qwen 2.5 7B? LLaMA 3.1 8B instruct? Mistral 7B v0.3? Q4_K_M or Q5_K_S quantization? The wrong choice means garbage output or painfully slow inference.

❌

Gateway configuration for local endpoints

OpenClaw's gateway needs specific settings for local inference — context window limits, token generation params, model routing rules. Get it wrong and your AI either hallucinates or crashes.

❌

Performance tuning is hardware-specific

GPU layers, thread count, batch size, context length — every machine needs different settings. Forums are full of “works for me” configs that won't work for you.

Or: 45-90 minutes with me.

I've tested dozens of model + config combinations with OpenClaw. I know what works, what breaks, and exactly how to tune it for your hardware. You get a working setup instead of a weekend-long debugging session.

Frequently Asked Questions

Can my computer actually run a local LLM?+
Probably yes. If you have a Mac with Apple Silicon (M1 or newer), or a PC with a dedicated NVIDIA GPU (8GB+ VRAM), you're good. Even CPU-only machines can run smaller models. During our session, I'll benchmark your hardware and pick the best model for your setup.
How does local quality compare to Claude or GPT-4?+
For everyday tasks — email drafting, scheduling, research, chat — local 7B-14B models are surprisingly good. For complex reasoning, coding, or creative writing, cloud models still have an edge. That's why our Local Pro package includes hybrid routing: local for 80% of tasks, cloud API for the hard stuff. Best of both worlds.
What about tool calling with local models?+
This is actually the #1 thing that trips people up with DIY setups. Tool calling (letting the AI use your calendar, email, web search, etc.) requires specific model support and configuration. Not every model handles it well. I've tested extensively and know exactly which models and settings work reliably with OpenClaw's tool system.
Do I still need any API keys?+
For pure local: no. Zero API costs, zero external dependencies. If you go with the hybrid setup (Local Pro), you'll use a cloud API as a fallback for complex tasks — typically $5-15/month instead of $50-200/month since most work stays local.
Will it work on my Mac?+
Apple Silicon Macs (M1, M2, M3, M4) are actually fantastic for local LLMs thanks to unified memory. An M1 Mac with 16GB can comfortably run 7B models. M2 Pro/Max and above can handle larger models. I'll optimize the setup specifically for your chip.
How much disk space do I need?+
A typical 7B model takes about 4-8GB. A 14B model takes 8-16GB. I recommend having at least 20GB free, more if you want multiple models available. We'll manage all of this during the session.
Can I switch between local and cloud models?+
Yes — that's a core feature of the Local Pro setup. You can route different tasks to different models. Quick questions go to your fast local model, complex analysis goes to Claude or GPT-4, and you control the rules. It's like having a team of AI assistants.
What if a new model comes out? Do I need another session?+
Nope. Part of what I teach you during the session is how to swap models. It's a one-line change in Ollama. I'll show you how to pull new models, test them, and switch your OpenClaw config. You'll be self-sufficient.

Stop Paying API Rent.

Your hardware is powerful enough. Your data deserves to stay local. Let's get you set up.

View Local PackagesAsk a Question

100% satisfaction guarantee on all packages. If your local setup doesn't work, you don't pay.