Optimized Openclaw configuration for low end devices with local llm.

Hi there! This is a guide of setting best configuration of OpenClaw to make it work on a low tier device with a local, small LLM model.

OpenClaw is a open source AI agent framework that acts as a 24/7 autonomous personal assistant. It works on top of llm model, which it uses for its inference/thinking.

My Device Specifications:

Before starting, here are the hardware specifications of the device used for this setup:

CPU: Ryzen 5 7430u, 6 Cores (TDP increased to 35W, from base 15W)

GPU: iGPU Vega 7 (Unified/Shared VRam)

RAM: 16 GB

OS: Arch Linux

LLM Search:

Finding the best suited llm for was the hardest part. Having a model with greater number of parameters reduces token processing and generation per second and model output drastically, causing system resource bloat and leaving no compute power for OpenClaw to execute commands, tools, and other background processes. Similarly, low parameter models easily hallucinate when provided with the large tool context required by OpenClaw.

I tried different LLM models ranging from 1.5B to 7B parameters, including:

DeepSeek-R1 1.5B

Microsoft Phi-3 Mini 3B

Alibaba Qwen Coder 2.5 3B

Salesforce xLam 1B

Alibaba Qwen Coder 2.5 7B

Google Gemma 4 E4B

Google Gemma 4 E4B (4-bit Quantized):

The best working model I found was Gemma 4 E4B (4-bit Quantized) when inferenced through llama.cpp and combined with OpenClaw. Following were the perfomance metrices and configuration for the model.

Prompt Processing: 165+ tokens/s (Reduces with context)

Prompt Generation: 11+ tokens/s

RAM Usage: 4–6 GB (Leaves room for around 2–3GB more)

Token Context Limit: 32k/64k

Temperature: 0.2–0.5

Reasoning: Off / Minimum

OpenClaw Configuration:

To keep the system stable and responsive, the OpenClaw configuration was tuned as follows:

Skills Loaded: 13+ skills, along with agent-browser from Clawhub for browser automation.

Text-to-Speech (TTS): Microsoft Edge

Speech-to-Text (STT): OpenAI Whisper Base.en

Channel: WhatsApp (Seperate Phone Number)

Context Profile: Minimum, ~16k Tokens (Important! This is the amount of context injected during the first prompt)

General Settings

Context Profile

Setup Guide:

Follow these sequential steps to configure and launch your optimized local environment:

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1.Download the Model:

Download the GGUF format of Google Gemma 4 E4B with 4-bit quantization from Hugging Face (file size is around 5.15GB approx).

Hugging Face

2.Configure llama.cpp:

Make sure to use the proper version of llama.cpp. I used the Vulkan (GPU) build as it is best suited for my device. Depending on your hardware other options like CUDA (Nvidia GPU) or HIP (AMD GPU) are available.

You can find more info at the official repository: https://github.com/ggml-org/llama.cpp

LLama.cpp Official Repository

3.Run the LLM Server:Local Inference.

Run the LLM model through the llama-server. Specify your model path, port, context limit, and set ngl to 99 to offload all layers to the GPU.

Note: You can reduce the ngl value if your entire model does not fit into VRAM (in case your available RAM is less than 8GB).

Run the following command on Arch Linux to launch the server.

llama-server --model "PATH_TO_MODEL" -ngl 99 --port 3044 -c 64380

Terminal

4.Configure OpenClaw Onboarding:Integration.

During the OpenClaw onboarding process, set up the OpenAI model provider.

Then, change its API URL to http://localhost:[PORT_NUMBER]/v1 and set the model name to llama-local/gemma-4-E4B_q4_0-it (name of the model)

Although we can get a working setup after all these tweaks, keep in mind that it can still take some considerable time (a maximum of 2–3 minutes) for responses where the system has to process and go through all of the context again. But it works.

Result