Running Local LLMs on Intel's Affordable iGPU: Performance Insights
I ran local LLMs on Intel's cheapest iGPU, and the results were surprisingly decent
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Image: Xda-developers
An experiment using the Intel N100 processor, one of the cheapest x86 options, demonstrated that local large language models (LLMs) can run effectively on low-end hardware. Using a LattePanda Mu with integrated graphics, the setup achieved decent performance for smaller models, challenging the notion that powerful GPUs are necessary for LLM tasks.
- 01The Intel N100 processor, paired with 8GB of RAM, was used to run local LLMs without a dedicated GPU.
- 02Using the llama.cpp inference engine, the setup managed to run models like Gemma 3 and DeepSeek R1-Distill-Qwen-7B with reasonable speeds.
- 03The configuration involved setting up an LXC container on a Proxmox machine, which allowed for effective use of the integrated graphics.
- 04Despite limitations, the N100 showed better performance than a Raspberry Pi for LLM tasks, particularly with a context window of 16K.
- 05The experiment suggests that affordable hardware can serve as a viable option for secondary LLM servers.
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In a recent experiment, the Intel N100 processor, known for its low cost, was tested for running local large language models (LLMs) without a dedicated graphics card. The setup utilized a LattePanda Mu with 8GB of RAM and integrated graphics, demonstrating surprisingly decent performance for light LLM tasks. The author opted for the llama.cpp inference engine over more resource-intensive options like Ollama, allowing for a more flexible deployment. After overcoming initial RAM limitations, the system successfully hosted models such as Gemma 3 and DeepSeek R1-Distill-Qwen-7B, achieving satisfactory inference speeds. Notably, the N100 outperformed a Raspberry Pi in running these models, with the ability to handle a context window of 16K without maxing out memory. While it may not replace high-end setups for larger models, the experiment indicates that budget-friendly hardware can effectively serve as a secondary LLM server, expanding accessibility for users without high-end computing resources.
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This experiment opens avenues for utilizing low-cost hardware for AI tasks, making technology more accessible.
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