What are the hardware requirements for GLM 5.3 W4A16?

GLM 5.3 W4A16 requires approximately 73.9 GB of VRAM and supports a context window of 1024K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 W4A16 and how large is it?

GLM 5.3 W4A16 is developed by JANGQ-AI, a model with 753.3b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate GLM 5.3 W4A16?

GLM 5.3 W4A16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM 5.3 W4A16 by JANGQ-AI — VRAM 73.9GB, 1024K context

Name: GLM 5.3 W4A16
Author: JANGQ-AI

GLM 5.3 W4A16 is an open-source language model by JANGQ-AI. Features: 753.3b LLM, VRAM: 73.9GB, Context: 1024K, License: other, MoE, LLM Explorer Score: 0.29.

Base model:quantized:zai-org/g... Base model:zai-org/glm-5.3 Compressed-tensors Conversational En Endpoints compatible Glm Glm-moe-dsa Glm moe dsa H200 Hopper Int4 Jang Marlin Moe Mtp Region:us Safetensors Sharded Tensorflow Vllm W4a16 Zh

Model Card on HF 🤗: https://huggingface.co/JANGQ-AI/GLM-5.3-W4A16

GLM 5.3 W4A16 Parameters and Internals

LLM Name	GLM 5.3 W4A16
Repository 🤗	https://huggingface.co/JANGQ-AI/GLM-5.3-W4A16
Base Model(s)	zai-org/GLM-5.3 zai-org/GLM-5.3
Model Size	753.3b
Required VRAM	73.9 GB
Updated	2026-08-31
Maintainer	JANGQ-AI
Model Type	glm_moe_dsa
Model Files	5.3 GB: 1-of-282 1.6 GB: 2-of-282 1.5 GB: 3-of-282 1.5 GB: 4-of-282 1.8 GB: 5-of-282 1.5 GB: 6-of-282 1.5 GB: 7-of-282 1.5 GB: 8-of-282 1.8 GB: 9-of-282 1.5 GB: 10-of-282 1.5 GB: 11-of-282 1.6 GB: 12-of-282 1.8 GB: 13-of-282 1.5 GB: 14-of-282 1.5 GB: 15-of-282 1.8 GB: 16-of-282 1.5 GB: 17-of-282 1.5 GB: 18-of-282 1.5 GB: 19-of-282 1.8 GB: 20-of-282 1.5 GB: 21-of-282 1.5 GB: 22-of-282 1.5 GB: 23-of-282 1.8 GB: 24-of-282 1.5 GB: 25-of-282 1.5 GB: 26-of-282 1.8 GB: 27-of-282 1.5 GB: 28-of-282 1.5 GB: 29-of-282 1.5 GB: 30-of-282 1.8 GB: 31-of-282 1.5 GB: 32-of-282 1.5 GB: 33-of-282 1.5 GB: 34-of-282 1.8 GB: 35-of-282 1.5 GB: 36-of-282 1.5 GB: 37-of-282 2.4 GB: 38-of-282 1.5 GB: 39-of-282 1.5 GB: 40-of-282 1.5 GB: 41-of-282 1.8 GB: 42-of-282 1.5 GB: 43-of-282 1.5 GB: 44-of-282
Supported Languages	en zh
Model Architecture	GlmMoeDsaForCausalLM
License	other
Context Length	1048576
Model Max Length	1048576
Transformers Version	5.15.0
Vocabulary Size	154880

Rank the GLM 5.3 W4A16 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

Best Alternatives to GLM 5.3 W4A16

Best Alternatives	Context / RAM	Downloads	Likes
GLM 5.3	1024K / 232.2 GB	8804	1255
GLM 5.2	1024K / 232 GB	2391730	4875
GLM 5.3 CYBERSECURITY FP8	1024K / 112.6 GB	227	29
GLM 5.3 MXFP4	1024K / 132.2 GB	649	1
GLM 5.3 BF16	1024K / 237.4 GB	482	24
GLM 5.3	1024K / 237.4 GB	379	6
GLM 5.3	1024K / 232.2 GB	200	1
GLM 5.3	1024K / 232.2 GB	81	1
GLM 5.2	1024K / 237.4 GB	1671	26
GLM 5.2 MXFP4xMXFP8	1024K / 68.1 GB	296	2

Note: green Score (e.g. "73.2") means that the model is better than JANGQ-AI/GLM-5.3-W4A16.