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Category: Local Model Setup

OpenClaw Local Model Setup details the architecture, quantization protocols, and local inference engine configurations required to execute OpenClaw agents entirely on local hardware with zero ongoing API costs and complete data privacy.

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Foundational Guides
Architecture & Protocols
Topic Directory
Frequently Asked Questions

Foundational Pillars (Start Here)

Authoritative implementation blueprints and core configurations verified by our technical engineering team.

Zero-Cost Inference

OpenClaw Ollama: Run 100% Local LLMs With No API Cost

How to pair OpenClaw with an Ollama local server running Llama 3, Qwen 2.5, or Mistral with zero external network calls.

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GUI Local Server

OpenClaw LM Studio: Run Local Models With Zero Cloud Cost

Configuring LM Studio’s local OpenAI-compatible inference server to power OpenClaw agents with customized GGUF quantizations.

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Cost Architecture

OpenClaw Local LLM: Cut API Costs With On-Device Models

Technical evaluation calculating hardware break-even periods and memory bandwidth requirements for local agent fleets.

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On-Device Inference: GGUF Quantization, VRAM Allocation & Context Limits

Running autonomous agents on local hardware unlocks two transformative benefits: total data sovereignty (no proprietary corporate data or code leaves your local network) and zero marginal token costs. However, local inference requires careful engineering around memory bandwidth and quantization precision. Quantized models (GGUF format at Q4_K_M or Q5_K_M) compress FP16 weights to 4 or 5 bits with minimal degradation in reasoning capability.

The primary performance determinant is VRAM (Video RAM) or Apple Unified Memory. The entire model weight payload plus the active KV-cache (context memory) must fit within fast graphics memory. If a model overflows into system RAM or swap storage, token generation speeds collapse from 30+ tokens/second to unworkable single-digit rates.

Model Size & Quant	Required Memory (Weights + KV)	Target Hardware	Agentic Capability
7B / 8B (Q4_K_M)	6 GB – 8 GB VRAM	NVIDIA RTX 3060 / Apple M1 16GB	Basic classification, single-turn tool calls, summaries
14B (Q4_K_M)	10 GB – 12 GB VRAM	NVIDIA RTX 4070 / Apple M2 16GB+	Reliable multi-step tool reasoning & structured output
32B (Q4_K_M)	20 GB – 24 GB VRAM	NVIDIA RTX 3090/4090 / Apple M4 24GB+	Near-frontier planning, complex code generation & analysis
70B (Q4_K_M)	42 GB – 48 GB VRAM	Dual RTX 3090 / Apple Studio 64GB+	Enterprise-grade autonomous swarms with zero cloud API

Curated Topic Directory & Implementation Guides

Explore specific technical tutorials, integration setups, and architectural deep-dives.

Local Inference Engines

Model Tuning & Switching

Frequently Asked Questions

Direct technical answers to common architecture, performance, and deployment questions.

Does OpenClaw support Ollama natively?

Yes. Ollama provides an OpenAI-compatible endpoint (typically `http://localhost:11434/v1`) that connects directly into OpenClaw’s model configuration without additional middleware.

Can local models reliably output structured JSON for tool use?

Modern open-weight models trained on instruction tuning (such as Qwen 2.5 and Llama 3.1/3.3 at 14B or larger) excel at tool calling when paired with structured output formats or grammar-guided generation.

Is an internet connection required to run OpenClaw locally?

No. When paired with Ollama or LM Studio, OpenClaw can run in an air-gapped environment with zero external network connectivity, ensuring total privacy.

How does local inference speed affect agent execution?

Cloud APIs typically generate 60-100 tokens/second. A local model running at 25-40 tokens/second on an Apple M4 or RTX 4070 feels responsive and completes standard tool loops in 2 to 5 seconds.