How Much RAM Does OpenClaw Need? (2026 Guide)
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One of the most frequent questions we get from new OpenClaw users, and even seasoned veterans scaling up their operations, is about RAM. How much do you really need? The answer, as always in the technical world, is “it depends.” But let’s be blunt: that’s not helpful enough. This guide aims to give you concrete, practical advice, complete with real-world scenarios, command examples, and a clear breakdown for every major use case in 2026.
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OpenClaw is designed to be lean and modular. Its core function is to orchestrate AI tasks, manage conversational states, route requests, and integrate various tools and models. This means its own footprint is surprisingly small, but the resources it *orchestrates* can be incredibly hungry. Understanding this distinction is key to sizing your system correctly.
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OpenClaw’s Core Footprint: The Foundation
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Let’s start with the OpenClaw process itself. As a Node.js application, OpenClaw is remarkably efficient at idle. You’ll find the core process typically consuming around 200-400MB of RAM. This covers the runtime, its loaded modules, and basic operational overhead. This is your baseline, regardless of what you connect it to.
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For a bare-bones installation, where OpenClaw is the only significant application running on a lightweight Linux distribution, you could technically get away with 512MB of RAM. However, this leaves virtually no room for the operating system, file caching, or any other background processes. We strongly advise against this for anything beyond initial testing.
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A more realistic minimum for a stable, responsive OpenClaw instance is 1GB of RAM. This provides comfortable headroom for the OS (e.g., a modern Linux server install might take 300-500MB), the OpenClaw process, and a bit of buffer for minor system tasks. If you’re running OpenClaw inside a Docker container, this 1GB minimum still applies to the container’s allocated memory.
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# Example: Checking OpenClaw's RAM usage on Linux\n# First, find the PID of your OpenClaw Node.js process\nps aux | grep openclaw | grep -v grep\n\n# Let's say the PID is 12345\n# Use 'htop' for an interactive view or 'smem' for more detail (install if needed)\nhtop -p 12345\n# Or, for a quick check:\ncat /proc/12345/status | grep VmRSS\n
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Scenario 1: Cloud-Based AI Models Only (Lean & Agile)
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This is the simplest and often most cost-effective scenario. You’re using OpenClaw as a sophisticated routing layer and state manager, but all the heavy AI lifting is done by external APIs like OpenAI’s GPT-4, Anthropic’s Claude 3, or Google’s Gemini. Your system just sends requests and receives responses.
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For this use case, 2GB of RAM is more than sufficient. This covers:
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- OpenClaw core process (~300MB)
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- Operating system overhead (~500MB for a server OS like Ubuntu Server or AlmaLinux)
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- Headroom for network operations, SSL handshakes, and buffering API responses
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- Potential light browser automation (e.g., a headless Chrome instance for a quick login before making an API call), which might temporarily spike usage.
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Real-world example: You’re building an internal customer support bot that routes queries to Claude 3 Opus, stores conversation history in a PostgreSQL database (hosted externally), and uses OpenClaw to handle user interaction and tool calling. A DigitalOcean Droplet with 2GB RAM (e.g., Basic Droplet, $14/month for 2vCPUs, 2GB RAM) or an AWS t2.small (2GB RAM, ~$15-20/month) would easily handle this for a moderate load of concurrent users.
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Scenario 2: Standard Use with Local Operations (The Sweet Spot)
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Most OpenClaw users fall into this category. You’re leveraging cloud models, but also performing local tasks like file processing, data transformation, simple database interactions, or more involved browser automation. This is where OpenClaw starts to shine as a true automation platform.
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For these mixed workloads, 4GB of RAM is the sweet spot. This provides ample room for:
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- OpenClaw process and OS
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- Multiple concurrent tasks (e.g., parsing several large JSON files, uploading results to S3)
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- Robust browser automation (e.g., using Puppeteer or Playwright to scrape data from a complex website, which can be RAM-intensive, often consuming 100-300MB per browser instance)
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- Running lightweight local services or micro-databases (e.g., SQLite, Redis for caching)
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Real-world example: An OpenClaw agent that takes user input, uses GPT-4 for initial classification, then scrapes product data from an e-commerce site using Playwright, processes the scraped HTML locally to extract specific fields, and finally stores the structured data in a local SQLite database before reporting back. Here, the Playwright browser instance and data processing will be the primary RAM consumers beyond OpenClaw itself. A VM with 4GB RAM (e.g., an AWS t3.medium or a slightly larger DigitalOcean Droplet) offers excellent value for this kind of work.
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Scenario 3: Integrating Local AI Models (The RAM Hogs)
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This is where RAM requirements can skyrocket, and it’s also where OpenClaw offers tremendous power and cost savings for specific tasks. Running Large Language Models (LLMs) locally, especially for CPU inference, is extremely RAM-intensive because the entire model weights, plus the context window, must be loaded into memory.
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The key factor here is the model size (e.g., 7B, 13B, 70B parameters) and its quantization level (e.g., Q4_K_M, Q8_0). Quantization reduces the precision of the model weights, making them smaller and faster, but with a slight hit to quality. OpenClaw typically integrates with local models via inference engines like Ollama or Llama.cpp.
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Sub-Scenario 3a: Small Local Models (7B-13B Quantized)
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For models like LLaMA-3 8B Q4_K_M or Mi
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