Can I Run OpenClaw With 8GB RAM and 8GB VRAM?
Yes, you can test OpenClaw on a machine with 8GB system RAM and a small GPU, but it is a small-model setup even if the GPU has 10GB, 12GB, or 16GB VRAM. Use it for learning, simple local chat, and basic tool-calling tests. For reliable autonomous OpenClaw work, upgrade system RAM or use a cloud model.
Direct Answer
With 8GB system RAM and 8GB VRAM, OpenClaw can run, but this is the lower edge for local AI.
The GPU memory is enough for small Q4 models. The system RAM is the real constraint. OpenClaw is not only the model: it also runs a Node process, tool calls, shell commands, browser or file workflows, logs, and whatever else your operating system already has open.
The same warning applies if you have 8GB system RAM with 10GB, 12GB, or 16GB VRAM. More VRAM can hold a larger model, but it does not give OpenClaw more system memory for browser automation, terminal tools, vector stores, logs, or context.
Use this setup for:
- Learning OpenClaw locally.
- Testing Ollama integration.
- Small prompts and short tool-calling experiments.
- Private local chat with small models.
Do not use this setup for:
- Long autonomous runs.
- Large repositories.
- Browser automation plus local inference.
- Multi-agent workflows.
- Anything that must keep tool-call JSON clean for hours.
What Will Fit
Start with one of these:
ollama pull qwen3:8b openclaw config set agents.defaults.models.chat ollama/qwen3:8b
or:
ollama pull llama3.2:8b openclaw config set agents.defaults.models.chat ollama/llama3.2:8b
These models fit the memory budget, but they are not the most reliable OpenClaw agent models. They are useful for confirming your local stack works before you spend money on more RAM, a bigger GPU, or cloud API usage.
What Will Not Fit Well
On this tier, skip:
| Model tier | Why |
|---|---|
| 14B Q8 or larger | Too tight for 8GB VRAM once context and overhead are included |
| 27B models | Not realistic on 8GB VRAM |
| 70B models | Completely outside this tier |
| Long-context runs | KV cache and tool output will pressure memory quickly |
If you have 12GB or 16GB VRAM, some 14B-class models may fit on the GPU. The problem is that OpenClaw still has to run outside the model. With only 8GB system RAM, the machine can stall even while GPU memory looks available.
If you see swapping, frozen browser tools, or empty OpenClaw responses, the machine is likely memory-bound rather than model-bound.
Safe Settings
Keep the context short:
openclaw config set agents.defaults.context_limit 4096 openclaw config set agents.defaults.keep_alive 5m
Close browser tabs and heavy desktop apps before running local inference. If the GPU is active but the whole machine feels slow, system RAM is probably saturated.
Upgrade Path
If this setup almost works, upgrade in this order:
- Move from 8GB system RAM to 16GB or 32GB.
- Keep the 8GB GPU for small-model testing.
- Move to 16GB VRAM only when you want stronger 14B-27B models.
- Use a cloud API for reliable OpenClaw work before buying hardware only for one project.
The first meaningful quality jump is not 8GB to 12GB VRAM. It is enough system RAM that OpenClaw and your tools can breathe. A balanced budget machine with 32GB system RAM and 8GB to 12GB VRAM will usually feel better than an 8GB RAM machine with a bigger GPU.
When To Use Cloud Instead
Use a cloud model if the task involves a real business workflow, repository edits, browser automation, or unattended execution. The 8GB / 8GB setup is good for learning the shape of OpenClaw. It is not where you should judge whether OpenClaw is reliable.
See Also
- OpenClaw Local Model Calculator
- Can I Run OpenClaw With 16GB RAM?
- Best Local LLMs for 16GB RAM
- Can My Computer Run a Local LLM?
- OpenClaw API Costs Compared
Related guides
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