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

oMLX significantly outperforms Ollama in Mac LLM inference speed

By PulseAugur Editorial · [2 sources] ·

A performance comparison between oMLX and Ollama for running LLMs locally on Mac devices revealed significant speed differences. oMLX, utilizing Apple Silicon's MLX framework, demonstrated a 35% faster token generation speed and a 7x improvement in multi-turn conversation latency compared to Ollama, which uses the GGUF backend. While oMLX offers specialized features like SSD KV Cache and Continuous Batching, Ollama maintains an advantage in cross-platform compatibility and a larger model ecosystem. AI

IMPACT oMLX's superior performance on Mac could accelerate local LLM adoption for developers and users prioritizing speed and responsiveness, especially for agentic applications.

RANK_REASON The article presents a detailed technical comparison and benchmark results of two LLM inference engines on a specific hardware platform, akin to a research study.

Read on dev.to — LLM tag →

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

oMLX significantly outperforms Ollama in Mac LLM inference speed

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The article presents a detailed technical comparison and benchmark results of two LLM inference engines on a specific hardware platform, akin to a research study.
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infra, product
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105 days old
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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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