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MacBook M1 Pro benchmarks Qwen and Nemotron models

By PulseAugur Editorial · [1 sources] ·

A user has benchmarked the performance of several large language models, including Qwen and Nemotron variants, on a 2021 MacBook Pro with an M1 Pro chip and 16GB of RAM. The benchmarks, conducted using the Kapitsa inference engine optimized for Apple Silicon, measured decoding and prefill speeds for models ranging from 4B to 35B parameters. The results indicate that even older MacBook models can effectively run these models for coding tasks, with specific optimizations for Apple's Metal kernels. AI

IMPACT Demonstrates the capability of consumer-grade hardware to run increasingly powerful open-source LLMs for coding tasks.

RANK_REASON User-generated benchmark of open-source models running on consumer hardware.

Read on r/LocalLLaMA →

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

MacBook M1 Pro benchmarks Qwen and Nemotron models

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User-generated benchmark of open-source models running on consumer hardware.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, product
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1 days old
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COVERAGE [1]

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

    How fast can a rusty Macbook M1 Pro 16Gb 2021 actually code with Qwen / Nemotron models? I ran benchmarks for Qwen3.5 4B, 9B Q4s, 35B; Nemotron Dory 4B, Nano 9B/v2.

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xcimmo/how_fast_can_a_rusty_macbook_m1_pro_16gb_2021/"> <img alt="How fast can a rusty Macbook M1 Pro 16Gb 2021 actually code with Qwen / Nemotron models? I ran benchmarks for Qwen3.5 4B, 9B Q4s, 35B; Nemotro…

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