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WinterMix quantization method enhances Qwen3.5-122B-A10B performance on MLX

By PulseAugur Editorial · [1 sources] ·

A new quantization method called WinterMix has been developed for MLX models, specifically targeting Qwen3.5-122B-A10B. This method results in an 82 GiB build that outperforms larger 6-bit builds and is nearly on par with the source GGUF model. A smaller 68 GiB build is also available, optimized for running multiple agent sessions concurrently on Apple Silicon hardware. AI

IMPACT Improves efficiency and performance of large language models on Apple Silicon, enabling more complex agentic workflows locally.

RANK_REASON Release of a new quantization method for an existing model, with performance benchmarks. [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 →

WinterMix quantization method enhances Qwen3.5-122B-A10B performance on MLX

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Release of a new quantization method for an existing model, with performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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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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model release, infra
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55 days old
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COVERAGE [1]

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

    [Release] WinterMix — Qwen3.5-122B-A10B in native MLX: an 82 GiB build that beats 94–95 GiB quants, plus a 68 GiB build for agent swarms

    <!-- SC_OFF --><div class="md"><p><strong>TL;DR:</strong> I spent 9 days developing a new quantization method for MLX models and measured 18 variants against each other on a single M5 Max MacBook Pro (128 GB). The result is the best-measuring MLX quant of Qwen3.5-122B-A10B I'm aw…

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