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⇱ GLM-4.5: Specifications and GPU VRAM Requirements


GLM-4.5

Active Parameters

355B

Context Length

128K

Modality

Multimodal

Architecture

Mixture of Experts (MoE)

License

MIT License

Release Date

28 Jul 2025

Knowledge Cutoff

Jan 2025

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

96

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

1,000,000

Sliding Window Attention

No

Sliding Window Size

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

5,120

Number of Layers

96

FFN Intermediate Size (Dense)

1,536

Multi-Token Prediction Heads

1

Tokenizer

Vocabulary Size

151,552

Mixture of Experts

Total Expert Parameters

32.0B

Number of Experts

160

Active Experts

8

Shared Experts

1

FFN Intermediate Size (per Expert)

1,536

Dense Layers Before MoE

3

Architecture Diagram

GLM-4.5

GLM-4.5 is a flagship multimodal large language model developed by Z.ai that integrates complex reasoning, software engineering, and agentic capabilities within a unified architecture. It employs a sophisticated Mixture-of-Experts (MoE) design with 355 billion total parameters, specifically engineered to optimize parameter efficiency by activating only 32 billion parameters during a forward pass. A defining feature of the model is its dual-mode execution framework, which allows it to alternate between a high-latency 'Thinking Mode' for multi-step planning and an instantaneous 'Non-Thinking Mode' for standard interactive tasks.

Technical innovations in GLM-4.5 focus on architectural depth over width to enhance logical deduction and mathematical processing. The model utilizes Grouped-Query Attention (GQA) with 96 attention heads and a hidden dimension size of 5120. Its MoE implementation features sigmoid-gated routing and QK-Norm to ensure stable expert utilization and load balancing. The training pipeline involved a massive 23-trillion-token corpus, including 7 trillion tokens dedicated to code and reasoning datasets, followed by reinforcement learning using the custom-built 'slime' infrastructure to refine autonomous decision-making.

Designed for production-grade agent applications, GLM-4.5 supports native function calling and complex web browsing with a high success rate. It features an expansive 128,000-token context window and a substantial maximum output limit of 96,000 tokens, making it suitable for long-form document analysis and full-stack software development. The model is released with open weights under the MIT License, facilitating broad adoption in both research and commercial environments.

About GLM Family

General Language Models from Z.ai


Other GLM Family Models

Evaluation Benchmarks

Rank

#66

BenchmarkScoreRank

Web Development

WebDev Arena

1410

30

Graduate-Level QA

GPQA

0.791

30

Professional Knowledge

MMLU Pro

0.81

32

General Text

Text Arena

1411

48

Rankings

Overall Rank

#66

Coding Rank

#50

Model Integrity

Total Score

B

69 / 100

GPU Requirements

Full Calculator

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Context Size: 1,024 tokens

1k
63k
125k

VRAM Required:

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