What are the hardware requirements for GLM 5.3 Flash BF16?

GLM 5.3 Flash BF16 requires approximately 237.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM 5.3 Flash BF16 and how large is it?

GLM 5.3 Flash BF16 is developed by zai-org, 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 BF16 perform on standard benchmarks?

GLM 5.3 Flash BF16 has the following published scores: LMSYS ELO 1259. Compare against reference models on this page or on the LLM Explorer leaderboards.

GLM 5.3 Flash BF16 by zai-org — VRAM 237.1GB

Name: GLM 5.3 Flash BF16
Author: zai-org

GLM 5.3 Flash BF16 is an open-source language model by zai-org. Features: 321.3b LLM, VRAM: 237.1GB, License: mit, LLM Explorer Score: 0.4, ELO: 1259.

Arxiv:2602.15763 Conversational En Endpoints compatible Glm5 next Image-text-to-text Region:us Safetensors Sharded Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/zai-org/GLM-5.3-Flash-BF16

GLM 5.3 Flash BF16 Benchmarks

LMSys ELO 1259 vs 1272 (so35) -1%

^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 BF16 Parameters and Internals

LLM Name	GLM 5.3 Flash BF16
Repository 🤗	https://huggingface.co/zai-org/GLM-5.3-Flash-BF16
Model Size	321.3b
Required VRAM	237.1 GB
Updated	2026-08-27
Maintainer	zai-org
Model Type	glm5_next
Model Files	5.4 GB: 1-of-120 5.4 GB: 2-of-120 5.4 GB: 3-of-120 5.4 GB: 4-of-120 5.4 GB: 5-of-120 5.4 GB: 6-of-120 5.3 GB: 7-of-120 5.4 GB: 8-of-120 5.4 GB: 9-of-120 5.4 GB: 10-of-120 5.4 GB: 11-of-120 5.4 GB: 12-of-120 5.4 GB: 13-of-120 5.4 GB: 14-of-120 5.4 GB: 15-of-120 5.4 GB: 16-of-120 5.4 GB: 17-of-120 5.3 GB: 18-of-120 5.4 GB: 19-of-120 5.3 GB: 20-of-120 5.3 GB: 21-of-120 5.4 GB: 22-of-120 5.4 GB: 23-of-120 5.4 GB: 24-of-120 5.4 GB: 25-of-120 5.4 GB: 26-of-120 5.4 GB: 27-of-120 5.4 GB: 28-of-120 5.4 GB: 29-of-120 5.4 GB: 30-of-120 5.4 GB: 31-of-120 5.4 GB: 32-of-120 5.4 GB: 33-of-120 5.4 GB: 34-of-120 5.4 GB: 35-of-120 5.4 GB: 36-of-120 5.4 GB: 37-of-120 5.4 GB: 38-of-120 5.4 GB: 39-of-120 5.3 GB: 40-of-120 5.4 GB: 41-of-120 5.4 GB: 42-of-120 5.4 GB: 43-of-120 5.4 GB: 44-of-120
Supported Languages	en zh
Model Architecture	Glm5NextForConditionalGeneration
License	mit
Transformers Version	5.16.0

Rank the GLM 5.3 Flash BF16 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

Quantized Models of the GLM 5.3 Flash BF16

Model	Likes	Downloads	VRAM
GLM 5.3 Flash MLX Q9	0	1093	352 GB
GLM 5.3 Flash EXL3 Q4	9	309	0 GB

Best Alternatives to GLM 5.3 Flash BF16

Best Alternatives	Context / RAM	Downloads	Likes
GLM 5.3 Flash	0K / 237.5 GB	34	1289
Apex Flash 1 Abliterated	0K / 460.9 GB	539	40
Apex Flash 1	0K / 460.9 GB	367	37
RED SNOW 5.3 FLASH BF16	0K / 215.5 GB	317	9
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 ABLITERATED FP8	0K / 226.7 GB	1465	18
GLM 5.3 Flash	0K / 242.5 GB	1034	15
GLM 5.3 Flash OQ2e Mtp	0K / 110.4 GB	218	1
GLM 5.3 Flash Nota NVFP4	0K / 21 GB	160	17

Note: green Score (e.g. "73.2") means that the model is better than zai-org/GLM-5.3-Flash-BF16.