[Benchmark] Kimi K2.7 Code Q3 on Mac Studio M3 Ultra + RTX PRO 6000 over llama.cpp RPC: prefill improves, no changes in token generation/decode
I came across this interesting article https://blog.exolabs.net/nvidia-dgx-spark/ while I don't have the DGX spark but it made me curious will this kind of arch speed up my setup for LLMs? Mac can host large models but the prefill speed sucks, so I tested in it on my setup for Ki
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This story from Reddit r/LocalLLaMA is relevant to the Open Source branch of the AI ecosystem and may affect models, products, or research direction.
Technical breakdown
I came across this interesting article https://blog.exolabs.net/nvidia-dgx-spark/ while I don't have the DGX spark but it made me curious will this kind of arch speed up my setup for LLMs? Mac can host large models but the prefill speed sucks, so I tested in it on my setup for Kimi 2.7. Short answer: it helps prefill, but it does not meaningfully help decode on this setup. RPC is still mostl
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