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A native Apple-silicon conversion of zai-org/GLM-5.3-Flash , quantised from the official checkpoint with its matching next-token prediction block preserved. Modules that are not eligible for MLX quantisation remain at their source-compatible precision. The checkpoint preserves the upstream tokenizer, chat template, multimodal processor, generation configuration, and native MTP metadata. This is a weight-only post-training quantisation. It does not retrain or fine-tune the upstream model.

Visual QA
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Visual QA
Input Text · Image→ Output Text
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Visual QA
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
Source / docs ↗

A native Apple-silicon conversion of nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 , quantized with stock 4-bit affine weights and packaged for MLX-LM and oMLX. This repository contains a stock 4-bit affine MLX conversion of NVIDIA Nemotron 3.5 Lightning. The source is a 30B-total / 3B-active hybrid mixture-of-experts model that interleaves Mamba-2, sparse MoE, and attention layers. The upstream tokenizer, chat template, and generation configuration are preserved. The decode figure is the median of three greedy 256-token runs after a 32-token Metal-kernel warm-up. It is a practical local reference, not a controlled cross-platform benchmark. Prompt length, context growth, sampler settings, memory pressure, thermal state, and MLX/oMLX versions can materially change performance.

Text generation
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A native Apple-silicon conversion of Qwen/Qwen3.8-27B , quantized with stock 4-bit affine weights for MLX-VLM and oMLX. This repository contains a stock 4-bit affine MLX conversion of Qwen3.8-27B. The upstream model is a dense, native vision-language model with flexible thinking control and support for text, images, and video. Its tokenizer, processor configuration, chat template, and generation configuration are preserved. The decode figure is the median of three greedy 256-token runs after a 256-token Metal-kernel warm-up. Individual runs measured 39.90, 39.89, and 39.91 tokens/s. This is a practical local reference, not a controlled cross-platform benchmark; prompt length, context growth, sampler settings, memory pressure, thermal state, and runtime versions can materially change performance.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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