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中文(ZH) oMLX vs Ollama Mac 本地推論Qwen3.5–35B實測

oMLX outpaces Ollama by 7x on Mac for local LLM inference

By PulseAugur Editorial · [2 sources] ·

A performance comparison between oMLX and Ollama for running the Qwen3.5-35B model on a Mac Studio M2 Max revealed significant speed differences. oMLX, utilizing Apple Silicon's native MLX framework, demonstrated a 35% faster token generation speed and a 7x reduction in multi-turn conversation latency compared to Ollama, which uses the GGUF backend. This performance gain is attributed to oMLX's optimized Metal kernels and efficient pipeline integration, particularly in handling prompt evaluation and continuous batching, including unique features like SSD KV Cache. AI

IMPACT oMLX offers a significant performance boost for local LLM inference on Macs, particularly for interactive applications like coding assistants, by drastically reducing multi-turn conversation latency.

RANK_REASON Comparative benchmark of two inference engines on specific hardware and model. [lever_c_demoted from research: ic=1 ai=0.7]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. dev.to — LLM tag TIER_1 中文(ZH) · JH5 ·

    oMLX vs Ollama Mac Local Inference Qwen3.5-35B Actual Test

    <h1> 同一顆 35B 模型，快 7 倍：oMLX vs Ollama Mac 本地推論完整對決 </h1> <blockquote> <p>Mac Studio M2 Max 96GB 上，同一顆 Qwen3.5-35B-A3B 模型的循序盲測比較</p> </blockquote> <p>Mac Studio M2 Max 跌 Ollama + Qwen3.5-35B，多輪對話延遲是 30 秒。換成 oMLX 同一顏模型，降到 4 秒——不是因為換了更強的模型，而是因為換了推論後端。</p> <p>這篇就是那次切換的完整測試紀錄。同一台機器、同一顆…

  2. dev.to — LLM tag TIER_1 中文(ZH) · JH5 ·

    oMLX vs Ollama Mac Local Inference Qwen3.5-35B Actual Test

    <h1> 同一顆 35B 模型，快 7 倍：oMLX vs Ollama Mac 本地推論完整對決 </h1> <blockquote> <p>Mac Studio M2 Max 96GB 上，同一顆 Qwen3.5-35B-A3B 模型的循序盲測比較</p> </blockquote> <p>Mac Studio M2 Max 跌 Ollama + Qwen3.5-35B，多輪對話延遲是 30 秒。換成 oMLX 同一顏模型，降到 4 秒——不是因為換了更強的模型，而是因為換了推論後端。</p> <p>這篇就是那次切換的完整測試紀錄。同一台機器、同一顆…

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