What are the hardware requirements for GLM 5.3 Flash W4A16?

GLM 5.3 Flash W4A16 requires approximately 73.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 Flash W4A16 and how large is it?

GLM 5.3 Flash W4A16 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.

Where can I download or evaluate GLM 5.3 Flash W4A16?

GLM 5.3 Flash 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 Flash W4A16 by JANGQ-AI — VRAM 73.1GB

Name: GLM 5.3 Flash W4A16
Author: JANGQ-AI

GLM 5.3 Flash W4A16 is an open-source language model by JANGQ-AI. Features: 321.3b LLM, VRAM: 73.1GB, License: other, LLM Explorer Score: 0.27.

Base model:quantized:zai-org/g... Base model:zai-org/glm-5.3-fla... Compressed-tensors Conversational En Endpoints compatible Glm Glm5-next Glm5 next H200 Hopper Image-text-to-text Int4 Jang Marlin Moe Mtp Multimodal Region:us Safetensors Sharded Tensorflow Vllm W4a16 Zh

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

GLM 5.3 Flash W4A16 Parameters and Internals

LLM Name	GLM 5.3 Flash W4A16
Repository 🤗	https://huggingface.co/JANGQ-AI/GLM-5.3-Flash-W4A16
Base Model(s)	zai-org/GLM-5.3-Flash zai-org/GLM-5.3-Flash
Model Size	321.3b
Required VRAM	73.1 GB
Updated	2026-09-01
Maintainer	JANGQ-AI
Model Type	glm5_next
Model Files	4.2 GB: 1-of-120 1.5 GB: 2-of-120 1.5 GB: 3-of-120 1.8 GB: 4-of-120 1.5 GB: 5-of-120 1.5 GB: 6-of-120 1.7 GB: 7-of-120 1.5 GB: 8-of-120 1.8 GB: 9-of-120 1.5 GB: 10-of-120 1.5 GB: 11-of-120 1.8 GB: 12-of-120 1.5 GB: 13-of-120 1.5 GB: 14-of-120 1.8 GB: 15-of-120 1.5 GB: 16-of-120 1.5 GB: 17-of-120 1.7 GB: 18-of-120 1.5 GB: 19-of-120 1.6 GB: 20-of-120 1.6 GB: 21-of-120 1.5 GB: 22-of-120 1.8 GB: 23-of-120 1.5 GB: 24-of-120 1.5 GB: 25-of-120 1.8 GB: 26-of-120 1.5 GB: 27-of-120 1.5 GB: 28-of-120 2.1 GB: 29-of-120 1.5 GB: 30-of-120 1.5 GB: 31-of-120 1.8 GB: 32-of-120 1.5 GB: 33-of-120 1.8 GB: 34-of-120 1.5 GB: 35-of-120 1.5 GB: 36-of-120 1.8 GB: 37-of-120 1.5 GB: 38-of-120 1.5 GB: 39-of-120 1.7 GB: 40-of-120 1.5 GB: 41-of-120 1.5 GB: 42-of-120 1.8 GB: 43-of-120 1.5 GB: 44-of-120
Supported Languages	en zh
Model Architecture	Glm5NextForConditionalGeneration
License	other
Transformers Version	5.16.0

Rank the GLM 5.3 Flash 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 Flash W4A16

Best Alternatives	Context / RAM	Downloads	Likes
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
... 5.3 Flash CYBERSECURITY W4A16	0K / 71.6 GB	4022	8
GLM 5.3 Flash	0K / 242.5 GB	1034	15
GLM 5.3 Flash Nota NVFP4	0K / 21 GB	160	17
Cyberneurova GLM 5.3 Flash FP8	0K / 242.9 GB	183	2
GLM 5.3 Flash DERISKED BF16	0K / 237.1 GB	226	3
...Flash MXFP4 Mixed CT AutoRound	0K / 67.4 GB	195	1
GLM 5.3 Flash NVFP4	0K / 69.8 GB	143	0

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