GLM-5.3-Flash-MLX-2bit-MTP

<p align="center"> <a href="https://tensorfold.dev"> <img src="https://huggingface.co/spaces/TensorFold/README/resolve/main/tensorfold-logo.png" alt="TensorFold" width="160"> </a> </p> <div align="center"> <a href="https://huggingface.co/zai-org/GLM-5.3-Flash"> <img src="zai-logo.png" width="72" height="72" alt="Z.ai logo"> </a> </div> <p align="center"> <img src="https://img.shields.io/badge/Z.ai-GLM--5.3--Flash-111827?style=flat-square" alt="Z.ai GLM-5.3-Flash"> <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=flat-square&logo=apple&logoColor=white" alt="Apple silicon MLX"> <img src="https://img.shields.io/badge/Native_MTP-Depth_1-2563EB?style=flat-square" alt="Native MTP depth 1"> <img src="https://img.shields.io/badge/TensorFold-Hybrid_2--bit-6E56CF?style=flat-square&logo=huggingface&logoColor=white" alt="TensorFold hybrid 2-bit"> </p> <h1 align="center">GLM-5.3-Flash, hybrid MLX 2-bit with native MTP</h1> <p align="center"> A deterministic quality-protected MLX conversion of <a href="https://huggingface.co/zai-org/GLM-5.3-Flash">zai-org/GLM-5.3-Flash</a>, retaining the model's matching native next-token prediction layer. </p> <p align="center"> <a href="https://huggingface.co/zai-org/GLM-5.3-Flash">Original model</a> · <a href="https://z.ai/blog/glm-5.3-flash">Z.ai overview</a> · <a href="https://arxiv.org/abs/2602.15763">Technical report</a> · <a href="https://github.com/ml-explore/mlx">Apple MLX</a> · <a href="LICENSE">MIT licence</a> </p>

[!IMPORTANT] This is a hybrid 2-bit checkpoint, not a uniform Q2 build. Routed trunk experts use Q2, non-expert language projections use Q8, native-MTP expert projections use Q4, and sensitive components remain BF16. This allocation passed live generation where broader Q2 recipes did not.

At a glance

Precision recipe

This is weight-only post-training quantisation. It does not retrain or fine-tune the upstream model.

Runtime compatibility

GLM-5.3-Flash uses the glm5_next multimodal architecture and a native NextN/MTP block. Use a runtime that supports this architecture and per-module MLX quantisation metadata.

The current upstream chat template defaults to maximum reasoning effort. For short, direct answers, pass reasoning_effort: low through the chat-template arguments.

Download and use

hf download TensorFold/GLM-5.3-Flash-MLX-2bit-MTP \
  --local-dir ./GLM-5.3-Flash-MLX-2bit-MTP

Add the downloaded directory to a compatible oMLX model directory, refresh the model registry, and select the model. Native MTP is optional and uses draft depth 1.

For an OpenAI-compatible request through oMLX, include the upstream low-reasoning template option when you want a concise answer:

{
  "model": "GLM-5.3-Flash-MLX-2bit-MTP",
  "messages": [{"role": "user", "content": "What is the capital of France?"}],
  "chat_template_kwargs": {"reasoning_effort": "low"}
}

Apple M3 Studio performance

Each result is the median of three 512-token text-generation runs after a separate warm-up. Both modes used the same checkpoint, prompt, deterministic sampling settings, and current upstream chat template.

Native MTP improved median decode throughput by 3.21% in this test. All six 512-token runs produced the same output hash. MTP gains depend on the prompt and draft acceptance, so treat this as a practical reference for the tested Studio rather than a universal result.

Architecture

GLM-5.3-Flash combines KDA linear-attention layers with periodic sparse-attention layers, a sparse mixture-of-experts feed-forward stack, manifold-constrained hyper-connections, a vision encoder, and one native next-token prediction layer.

See the official model card, Z.ai overview, and GLM-5 technical report for upstream training, evaluations, intended uses, and safety guidance.

Validation

Limitations

• Hybrid quantisation can reduce quality relative to the official checkpoint. The effect depends on the workload.
• The configured one-million-token context does not mean every Apple silicon system has enough memory for a full-context request.
• Image and video prefill have different memory and throughput characteristics from text-only generation. The published throughput numbers are text-only.
• Native MTP may be neutral or slower on prompts with low draft acceptance.
• Runtime support for glm5_next, mixed per-module quantisation, and native MTP is version-sensitive.

This is a community quantisation, not an official Z.ai release.

Licence and attribution

The upstream model uses the MIT License. The official licence text is included as LICENSE.

Model design, training, upstream evaluations, and documentation belong to Z.ai and the GLM-5 contributors. The MLX conversion, native-MTP preservation, validation, and packaging are provided by TensorFold.

If you use this model in research, cite the upstream report:

@misc{glm5team2026glm5,
  title        = {GLM-5: from Vibe Coding to Agentic Engineering},
  author       = {GLM-5-Team and others},
  year         = {2026},
  eprint       = {2602.15763},
  archivePrefix= {arXiv},
  primaryClass = {cs.LG},
  url          = {https://arxiv.org/abs/2602.15763}
}

<!-- TensorFold-chooser-start -->

Choose for your Mac

64GB Macs · 128GB Macs · 256GB Macs

No measured memory tier is assigned here. The collections use published M3 Studio peaks with at least 25% nominal headroom; fit on other Macs is an estimate, and full context is not guaranteed. Start with short context and one request.

Runtime and evidence

The exact tested oMLX application version is not recorded here; a library version is not an app version. The original performance tables retain their benchmark conditions and speed figures; this documentation update adds no new test results.

Quick start and demo prompt

hf download TensorFold/GLM-5.3-Flash-MLX-2bit-MTP --local-dir ./models/GLM-5.3-Flash-MLX-2bit-MTP

Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.

Try this in a new chat with a 128-token output limit:

Explain why the sky looks blue in three short sentences.

This is a demo prompt to try, not a recorded successful run; a captured demonstration for this documentation update is not yet available.

Follow TensorFold for new Apple Silicon releases and fixes.

<!-- TensorFold-chooser-end -->