What are the hardware requirements for GLM 5.3 Flash JANG MTP?

GLM 5.3 Flash JANG MTP requires approximately 102.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 Flash JANG MTP and how large is it?

GLM 5.3 Flash JANG MTP is developed by JANGQ-AI, a model with 321.3b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does GLM 5.3 Flash JANG MTP perform on standard benchmarks?

GLM 5.3 Flash JANG MTP has the following published scores: LMSYS ELO 1473. Compare against reference models on this page or on the LLM Explorer leaderboards.

GLM 5.3 Flash JANG MTP by JANGQ-AI — VRAM 102.8GB

Name: GLM 5.3 Flash JANG MTP
Author: JANGQ-AI

GLM 5.3 Flash JANG MTP is an open-source language model by JANGQ-AI. Features: 321.3b LLM, VRAM: 102.8GB, License: mit, Quantized, LLM Explorer Score: 0.46, ELO: 1473.

Agent Apple-silicon Awq Base model:finetune:zai-org/gl... Base model:zai-org/glm-5.3-fla... Conversational En Glm5 next Image-text-to-text Imatrix Jang Mlx Moe Quantized Reasoning Region:us Safetensors Sharded Tensorflow Tool-use Video Vision

Model Card on HF 🤗: https://huggingface.co/JANGQ-AI/GLM-5.3-Flash-JANG-MTP

GLM 5.3 Flash JANG MTP Benchmarks

LMSys ELO 1473 vs 1272 (so35) 15.8%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

GLM 5.3 Flash JANG MTP Parameters and Internals

LLM Name	GLM 5.3 Flash JANG MTP
Repository 🤗	https://huggingface.co/JANGQ-AI/GLM-5.3-Flash-JANG-MTP
Base Model(s)	zai-org/GLM-5.3-Flash zai-org/GLM-5.3-Flash
Model Size	321.3b
Required VRAM	102.8 GB
Updated	2026-09-02
Maintainer	JANGQ-AI
Model Type	glm5_next
Model Files	5.9 GB: 1-of-27 0.2 GB: 2-of-27 5.8 GB: 3-of-27 3.1 GB: 4-of-27 5.8 GB: 5-of-27 3.1 GB: 6-of-27 5.8 GB: 7-of-27 1.7 GB: 8-of-27 5.4 GB: 9-of-27 3.7 GB: 10-of-27 5.8 GB: 11-of-27 3.0 GB: 12-of-27 5.9 GB: 13-of-27 3.1 GB: 14-of-27 6.0 GB: 15-of-27 3.7 GB: 16-of-27 5.7 GB: 17-of-27 4.0 GB: 18-of-27 5.4 GB: 19-of-27 4.3 GB: 20-of-27 5.7 GB: 21-of-27 3.1 GB: 22-of-27 5.7 GB: 23-of-27 0.3 GB: 24-of-27 0.1 GB: 25-of-27 0.2 GB: 26-of-27 0.3 GB: 27-of-27
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Glm5NextForConditionalGeneration
License	mit
Model Max Length	1048576
Transformers Version	5.16.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>

Rank the GLM 5.3 Flash JANG MTP capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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GLM 5.3 Flash Uncensored AWQ	0K / 176 GB	64	1
GLM 5.3 Flash	0K / 237.5 GB	34	1289
GLM 5.3 Flash BF16	0K / 237.1 GB	24	38
GLM 5.3 Flash FP8	0K / 237.5 GB	5048	22
GLM 5.3 Flash Nota NVFP4	0K / 21 GB	160	17
GLM 5.3 Flash	0K / 242.5 GB	1034	15
...Flash MXFP4 Mixed CT AutoRound	0K / 67.4 GB	195	1
GLM 5.3 Flash DERISKED BF16	0K / 237.1 GB	127	2
...Flash Abliterated Experimental	0K / 471.3 GB	69	3
GLM 5.3 Flash W4A16	0K / 73.1 GB	53	1

Note: green Score (e.g. "73.2") means that the model is better than JANGQ-AI/GLM-5.3-Flash-JANG-MTP.