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Kimi 2.7 Code benchmark shows limited decode speed gains with RTX GPU

By PulseAugur Editorial · [1 sources] ·

A benchmark test was conducted using the Kimi 2.7 Code model on a Mac Studio M3 Ultra with an NVIDIA RTX PRO 6000 GPU, leveraging llama.cpp for RPC communication. The results indicated that while using the RTX GPU improved prefill speeds by approximately 14.8%, it offered minimal gains of about 4.2% in token generation and decoding speeds. The overall request time saw a modest improvement of 12.3%. AI

IMPACT This benchmark provides insights into optimizing LLM performance on hybrid CPU-GPU setups, particularly for prefill operations.

RANK_REASON Benchmark results of an LLM configuration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Kimi 2.7 Code benchmark shows limited decode speed gains with RTX GPU

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Benchmark results of an LLM configuration. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/No_Run8812 ·

    [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

    <!-- SC_OFF --><div class="md"><p>I came across this interesting article <a href="https://blog.exolabs.net/nvidia-dgx-spark/">https://blog.exolabs.net/nvidia-dgx-spark/</a> while I don't have the DGX spark but it made me curious will this kind of arch speed up my setup for LLMs? …

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