What are the hardware requirements for GLM 5.3 Int4 Int8Mix?

GLM 5.3 Int4 Int8Mix requires approximately 62.2 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 Int4 Int8Mix and how large is it?

GLM 5.3 Int4 Int8Mix is developed by vikasclawd. 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 Int4 Int8Mix?

GLM 5.3 Int4 Int8Mix is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM 5.3 Int4 Int8Mix by vikasclawd — VRAM 62.2GB, 1024K context

Name: GLM 5.3 Int4 Int8Mix
Author: vikasclawd

GLM 5.3 Int4 Int8Mix is an open-source language model by vikasclawd. Features: LLM, VRAM: 62.2GB, Context: 1024K, License: other, MoE, LLM Explorer Score: 0.27.

Base model:quantized:zai-org/g... Base model:zai-org/glm-5.3-bf1... Compressed-tensors Conversational Dgx-spark Glm-5.3 Glm moe dsa Int4 Int8 Mixed-precision Moe Quantization Quanttrio Region:us Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/vikasclawd/GLM-5.3-Int4-Int8Mix

GLM 5.3 Int4 Int8Mix Parameters and Internals

LLM Name	GLM 5.3 Int4 Int8Mix
Repository 🤗	https://huggingface.co/vikasclawd/GLM-5.3-Int4-Int8Mix
Base Model(s)	zai-org/GLM-5.3-BF16 zai-org/GLM-5.3-BF16
Required VRAM	62.2 GB
Updated	2026-09-01
Maintainer	vikasclawd
Model Type	glm_moe_dsa
Model Files	5.0 GB: 1-of-282 1.4 GB: 2-of-282 1.4 GB: 3-of-282 1.4 GB: 4-of-282 1.5 GB: 5-of-282 1.4 GB: 6-of-282 1.4 GB: 7-of-282 1.4 GB: 8-of-282 1.5 GB: 9-of-282 1.4 GB: 10-of-282 1.4 GB: 11-of-282 1.4 GB: 12-of-282 1.5 GB: 13-of-282 1.4 GB: 14-of-282 1.4 GB: 15-of-282 1.5 GB: 16-of-282 1.4 GB: 17-of-282 1.4 GB: 18-of-282 1.4 GB: 19-of-282 1.5 GB: 20-of-282 1.4 GB: 21-of-282 1.4 GB: 22-of-282 1.4 GB: 23-of-282 1.5 GB: 24-of-282 1.4 GB: 25-of-282 1.4 GB: 26-of-282 1.5 GB: 27-of-282 1.4 GB: 28-of-282 1.4 GB: 29-of-282 1.4 GB: 30-of-282 1.5 GB: 31-of-282 1.4 GB: 32-of-282 1.4 GB: 33-of-282 1.4 GB: 34-of-282 1.5 GB: 35-of-282 1.4 GB: 36-of-282 1.4 GB: 37-of-282 1.7 GB: 38-of-282 1.4 GB: 39-of-282 1.4 GB: 40-of-282 1.4 GB: 41-of-282
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 Int4 Int8Mix capabilities

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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 Int4 Int8Mix

Best Alternatives	Context / RAM	Downloads	Likes
Glm 5.3 Exlr8 K2 K3 K4	1024K / GB	117649	12
GLM 5.2 Colibri Int4	1024K / 2.7 GB	7924	169
...Colibri Int4 G64 With Int8 Mtp	1024K / 2.8 GB	10526	43
Atria Dawn Preview MLX Q4i	1024K / 198.7 GB	400	2
GLM 5.3 NVFP4 CSF	1024K / GB	133	1
GLM 5.3 Colibri Int4 G64	1024K / 2.8 GB	25	1
GLM 5.2 NVFP4 TR3 Hybrid	1024K / 0.8 GB	338	3
GLM 5 FP8	198K / 243 GB	54	5

Note: green Score (e.g. "73.2") means that the model is better than vikasclawd/GLM-5.3-Int4-Int8Mix.