What are the hardware requirements for GLM 5.3 Flash AWQ W4A16?

GLM 5.3 Flash AWQ W4A16 requires approximately 176 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

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

GLM 5.3 Flash AWQ W4A16 is developed by wtdcode, 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 AWQ W4A16 perform on standard benchmarks?

GLM 5.3 Flash AWQ W4A16 has the following published scores: LMSYS ELO 1474. Compare against reference models on this page or on the LLM Explorer leaderboards.

GLM 5.3 Flash AWQ W4A16 by wtdcode — VRAM 176GB

Name: GLM 5.3 Flash AWQ W4A16
Author: wtdcode

GLM 5.3 Flash AWQ W4A16 is an open-source language model by wtdcode. Features: 321.3b LLM, VRAM: 176GB, Quantized, LLM Explorer Score: 0.51, ELO: 1474.

Awq Base model:quantized:zai-org/g... Base model:zai-org/glm-5.3-fla... Compressed-tensors Glm5 next Quantized Region:us Safetensors Sharded Tensorflow

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

GLM 5.3 Flash AWQ W4A16 Benchmarks

LMSys ELO 1474 vs 1272 (so35) 15.9%

^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 AWQ W4A16 Parameters and Internals

LLM Name	GLM 5.3 Flash AWQ W4A16
Repository 🤗	https://huggingface.co/wtdcode/GLM-5.3-Flash-AWQ-W4A16
Base Model(s)	zai-org/GLM-5.3-Flash zai-org/GLM-5.3-Flash
Model Size	321.3b
Required VRAM	176 GB
Updated	2026-09-04
Maintainer	wtdcode
Model Type	glm5_next
Model Files	20.0 GB: 1-of-9 20.0 GB: 2-of-9 20.0 GB: 3-of-9 20.0 GB: 4-of-9 20.0 GB: 5-of-9 20.0 GB: 6-of-9 20.0 GB: 7-of-9 20.0 GB: 8-of-9 16.0 GB: 9-of-9 4.3 GB 4.3 GB 4.3 GB 1.9 GB
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Glm5NextForConditionalGeneration
Model Max Length	1048576
Transformers Version	5.16.1
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>

Rank the GLM 5.3 Flash AWQ 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 AWQ W4A16

Best Alternatives	Context / RAM	Downloads	Likes
GLM 5.3 Flash JANG MTP	0K / 102.8 GB	277	1
...5.3 Flash Abliterated MLX 4bit	0K / 185.6 GB	911	2
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	134	3

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