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LatestZhipu AI5
Zhipu AIText / LLM

Zhipu AI Releases Open-Source GLM-5 MoE Model

The new Mixture-of-Experts model from the Chinese AI company combines an advanced architecture with a fully permissive MIT license for commercial use.

Feb 11, 2026
Major releaseMIT
GLM-5.3

Chinese AI research firm Zhipu AI has released GLM-5, a significant new open-source language model. This release marks a major addition to the open-source ecosystem, offering a sophisticated new architecture from a prominent AI company.

Efficient by Design

GLM-5 is built on a Mixture-of-Experts (MoE) architecture, a technique that improves computational efficiency by activating only specialized parts of the network for any given task. The model also incorporates sparse attention, further optimizing its performance and resource usage. This combination is designed to deliver powerful reasoning capabilities while maintaining manageable inference costs.

Perhaps most notably, Zhipu AI has released GLM-5 under the permissive MIT license, which allows for broad and unrestricted commercial application. The choice of such an open license for an advanced model encourages wide adoption and makes it a valuable asset for developers and businesses building with AI.

The release of GLM-5 provides a new, highly capable foundation for the open-source community, blending an efficient, modern architecture with the freedom to innovate and build commercial products.

Sources

  • zai-org/GLM-5

    Hugging Face

    Visit
OlderinclusionAI's Ming 2.0 Tackles Any-to-Any MultimodalityinclusionAI · Any-to-Any · 9 months agoNewerMiniMax Releases M2.5 Mixture-of-Experts ModelMiniMax · Text / LLM · 9 months ago

Get the model

Hugging Face

Specs

Active params40B active
Context window203K tokens
Size1.5 TB
PrecisionBF16
ArchitectureGlmMoeDsaForCausalLM
LicenseMIT
Downloads263.3K
Likes5.2K

Can you run it?

Datacenter only — about 454.1 GB even at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    1.5 TB
  • 8-bit

    Multi-node cluster

    801 GB
  • 4-bit

    8×H100 80 GB node

    454.1 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 754B parameters must be resident even though only 40B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning
4 versions — view changelog

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