Raspberry Pi Cluster Ran Large Language Model
A four-node setup achieved 15 tokens per second running a 30-billion parameter model on commodity hardware.
Updated on Oct. 7, 2026 in Artificial Intelligence
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Hellomatik has successfully configured a four-node cluster of Raspberry Pi 5 boards to run the Qwen3-30B-A3B language model. The research-stage system achieved a decode throughput of 15.143 tokens per second using tensor parallelism.
Why it matters
This experiment demonstrates the feasibility of running complex, multi-billion parameter models on low-power, distributed CPU hardware through targeted software optimizations. It highlights a path for executing large-scale inference tasks without specialized enterprise accelerators.
The cluster, utilizing four Raspberry Pi 5 boards with 16GB of LPDDR4X memory each, achieved a memory read bandwidth of 12.5GB/s per board. While it maintains 11.4GB/s of sustained DRAM traffic, the system still encounters backend-stalled CPU cycles reaching 49%.
The players
Hellomatik
A research laboratory focused on optimizing artificial intelligence software for distributed and low-power hardware environments.
The details
The implementation relies on tensor parallelism—a technique where a model's tensors are split across multiple computing nodes—to distribute the workload of the 30-billion parameter Qwen3-30B-A3B model. The software, running on a Debian 13 environment with a 6.12.75 aarch64 kernel, was modified with twelve source-level changes to the distributed-llama framework. It leverages Arm NEON dot-product instructions, which are specialized CPU operations designed to accelerate mathematical calculations, to handle the heavy computational load on the Broadcom BCM2712 processor.
Timeline
October 7, 2026: The research results were published.
The Tech Race
This development represents a departure from the industry standard of using high-bandwidth GPUs for large language model inference. It aligns with broader research efforts to enable efficient AI execution on edge computing platforms.
This configuration is a research-stage prototype and not currently available for commercial deployment. Users should note that very large prompts of 20,000 tokens may require more than 20 minutes to process, limiting its utility for real-time applications.
The takeaway
This study proves that commodity ARM-based boards can serve as functional hardware for significant language models if software overhead is sufficiently reduced. Researchers and developers should track future efforts to lower the 49% CPU backend-stall rate to see if throughput can be further increased.
Further reading
For more on the latest research in model inference, visit the Artificial Intelligence section.
Source note: This article includes information reported by LinuxGizmos.
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