GLM-5.3-Flash, MLX oQ4 with native MTP

A sensitivity-guided mixed-precision MLX conversion of zai-org/GLM-5.3-Flash, built from the official checkpoint with its matching next-token prediction block preserved.

Original model · Z.ai overview · Technical report · Apple MLX · MIT licence

At a glance

The upstream tokenizer, chat template, multimodal processor, generation configuration, and native MTP metadata are preserved.

oQ recipe

Sensitivity was measured with the built-in multilingual code calibration set, 128 samples at 256 tokens. The allocation rule was byte-budgeted layer-sensitivity ranking under the oQ4 target and hard cap. These details are part of the release recipe and should be used when comparing oQ variants.

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

Runtime compatibility

GLM-5.3-Flash uses the new glm5_next multimodal architecture, hybrid linear and sparse attention, manifold-constrained hyper-connections, and an embedded next-token prediction layer. The oQ layout also requires a runtime that understands its per-module quantisation metadata.

[!IMPORTANT] A runtime that can load an ordinary MLX conversion is not necessarily compatible with this oQ checkpoint or its embedded MTP layer. Use a build that supports the recorded per-module recipe, GLM5 Next drafting, verification, and cache reconciliation.

Download and use

hf download TensorFold/GLM-5.3-Flash-MLX-oQ4-MTP \
  --local-dir GLM-5.3-Flash-MLX-oQ4-MTP

In a compatible oMLX build:

1. Add the downloaded model directory.
2. Load it once with native MTP disabled to validate the oQ baseline path.
3. Enable Native MTP in the model settings and select the validated draft depth shown below.
4. Start a new conversation so the runtime rebuilds the model and caches with MTP active.

Apple M3 Studio performance

Measured on the same Apple M3 Studio, with identical prompts, sampling, context, output length, and warmed model state:

These figures are a local reference, not a cross-platform guarantee. Prompt shape, image input, context growth, draft acceptance, sampling, memory pressure, thermal state, and runtime revisions can materially change performance.

Architecture

GLM-5.3-Flash is Z.ai's first natively multimodal GLM-5 model. It combines KDA linear-attention layers with periodic sparse-attention layers, a sparse mixture-of-experts feed-forward stack, manifold-constrained hyper-connections, and a 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

Validation used oMLX 0.6.3rc3 build 2475, MLX 0.32.0, and MLX-VLM 0.6.3 with GLM5 Next runtime integration.

Limitations

• Mixed-precision quantisation can reduce quality relative to the official checkpoint. The effect can differ by task and by oQ allocation.
• Sensitivity scores are calibration-dependent; protected modules are not a guarantee of BF16-equivalent quality.
• Native MTP improves speed only when draft acceptance offsets drafting and verification cost. It may be neutral or slower for unpredictable output.
• The configured one-million-token context does not imply that every Apple-silicon system has enough memory for a full-context request.
• Image prefill has different memory and throughput characteristics from text-only generation and is not represented by the decode benchmark above.
• Runtime support for glm5_next, oQ metadata, and native MTP is evolving. Use the exact validated versions listed in this card.
• Validate numerical behaviour, output quality, safety, and tool use on representative workloads before production deployment.

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

Licence and attribution

The upstream model is released under the MIT License. The required licence text is included in this repository.

Model design, training, upstream evaluations, and documentation belong to Z.ai and the GLM-5 contributors. The oQ conversion, Apple-silicon validation, native-MTP integration work, 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}
}

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

oMLX version mentioned in the existing card: 0.6.3rc3; consult its compatibility notes for whether this was tested and any required integrations. 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-oQ4-MTP --local-dir ./models/GLM-5.3-Flash-MLX-oQ4-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.