Raspberry Pi Cluster Ran Large Language Model
A four-node cluster achieved significant throughput for the 30-billion parameter Qwen3 model.
Updated on Oct. 7, 2026 in Artificial Intelligence
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Hellomatik configured a cluster using four Raspberry Pi 5 boards to successfully run the Qwen3-30B-A3B language model. The system achieved a decode throughput of 15.143 tokens per second by utilizing custom tensor parallelism.
Why it matters
The experiment demonstrates how software optimizations can enable large-scale AI inference on modest CPU hardware. By reducing memory traffic, the setup highlights potential paths for running high-parameter models outside of specialized GPU environments.
The system utilizes four Raspberry Pi 5 boards, each equipped with 16GB of LPDDR4X memory and a Broadcom BCM2712 processor. Each node sustains a memory read bandwidth of 12.5GB/s.
The players
Hellomatik
This is a laboratory and research entity responsible for the development and testing of the Raspberry Pi AI cluster.
The details
The cluster, which includes one root coordinator and three worker nodes, relies on distributed-llama with twelve source-level changes to manage the 30 billion parameter model. Engineers applied Arm NEON dot-product instructions to improve efficiency, though processing a 20,000 token prompt may take over 20 minutes.
Timeline
October 7, 2026: Date of the project documentation.
The Tech Race
This achievement follows a pattern set by the distributed-llama implementation regarding optimizing LLM inference across low-power hardware. The project extends the capabilities of this software by applying custom source-level changes to boost performance.
For developers, this experiment proves that high-parameter models are increasingly accessible on standard, low-cost hardware rather than expensive GPUs. It suggests future workflows may prioritize software-level parallelization to lower the barrier for private AI hosting.
The takeaway
This project highlights that hardware limitations can be partially mitigated through aggressive software-level optimizations and tensor parallelism. Readers interested in local AI hosting should note that while performance is improving, prompt processing times for massive inputs remain a significant bottleneck.
Further reading
For more on developments in this field, visit the Artificial Intelligence section.
Source note: This article includes information reported by LinuxGizmos.
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