Introduction to Kimi K2 Thinking

Kimi K2 Thinking is a cutting-edge AI model that has been making waves in the tech community. Recently, a tester achieved an impressive 28.3 t/s on a 4x Mac Studio cluster, showcasing the model’s potential for high-performance computing.

Testing and Debugging

The tester was loaned a cluster of 4x Mac Studios (2x 512GB and 2x 256GB) by Apple until February. The initial testing phase was focused on debugging, as the RDMA support was still relatively new. However, now that the support is more stable, the tester can conduct more in-depth testing.

RDMA Tensor Setting and Llama.cpp RPC

The tester compared the performance of llama.cpp RPC and Exo’s new RDMA Tensor setting on the Mac Studio cluster. While the results are promising, the lack of a standardized benchmark like llama-bench in Exo makes direct comparisons challenging.

Smaller, More Efficient Models

The development of smaller, more efficient models is a key focus area in the AI community. These models can run on consumer hardware, making them more accessible to a wider audience. As Source 1 notes, ‘the future is smaller models’.

Hardware Advancements and RDMA

Advances in hardware, such as higher memory bandwidth and more RAM, are expected to make larger models more accessible on local hardware. The use of RDMA over Thunderbolt 5, as seen in Source 2, can significantly improve performance.

Running Kimi K2 Thinking Locally

For those interested in running Kimi K2 Thinking locally, Source 4 provides a step-by-step guide. The guide includes instructions on obtaining the latest llama.cpp and configuring the model for local use.