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Running a One Trillion-Parameter LLM Locally on AMD Ryzen AI Max+ Cluster

Trillion-Parameter LLM on an AMD Ryzenâ¢ AI Max+ Cluster

amd.com

March 1, 2026

14 min read

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Summary

A small-scale distributed inference cluster can be built using AMD’s Ryzen™ AI Max+ AI PC platform to run a one trillion-parameter Large Language Model. A four-node cluster of Framework Desktop systems demonstrates the local inference of the Kimi K2.5 open-source model.

Key Takeaways

A one trillion-parameter Large Language Model, Kimi K2.5, can be run locally on a four-node cluster of AMD Ryzen AI Max+ systems using llama.cpp RPC.
The maximum VRAM allocation for each node in the cluster can be increased to 120GB, allowing a total of 480GB across four nodes.
The Lemonade SDK provides pre-built binaries for easier setup of llama.cpp with AMD ROCm acceleration for Ryzen AI Max+ systems.
Kimi K2.5 is designed for software engineering tasks and can process multimodal inputs, including visual and video data.

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Community Sentiment

Negative

Positives

Running a one trillion-parameter LLM locally showcases significant advancements in AI capabilities, making high-performance models more accessible to enthusiasts.
The ability to run such a large model on consumer hardware indicates a promising trend towards democratizing AI technology.

Concerns

Performance metrics reveal that the system struggles with low throughput (<10 tps) and high latency, making it impractical for real-time applications.
The reported time-to-first-token of over a minute for an 8192 token prompt highlights severe inefficiencies compared to leading models like ChatGPT.
The hardware's limitations, such as non-upgradable components and slow networking options, raise concerns about its long-term viability and usability.