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Zhipu AI’s GLM-5 is making waves in the local LLM space. Here’s what developers need to know.
For context: GLM-5 beats DeepSeek-V3.2 on SWE-bench and comes close to Claude Opus 4.5 on coding tasks.
Architecture
GLM-5 uses a MoE (Mixture of Experts) design:
<ul>
<li>744B total parameters</li>
<li>40B active per token</li>
</ul>
This means you get large-model performance without running all 744B params at once. The model also uses DeepSeek Sparse Attention for efficient inference and was trained on 28.5T tokens.
Community Reaction
The r/LocalLLaMA response has been strong:
“I’m spooked by GLM-5”
“GLM-5 is a local GOAT”
Should You Use It?
Yes, if:
<ul>
<li>You need strong coding capabilities offline</li>
<li>You have the hardware (8+ GPUs recommended for FP8)</li>
<li>Privacy requirements prevent cloud APIs</li>
</ul>
Maybe not, if:
<ul>
<li>You’re happy with existing cloud-based solutions</li>
<li>You don’t have multi-GPU setup</li>
</ul>
Try it (Instant 10% off) : <a href="https://z.ai/subscribe?ic=C7UHL1TGWJ" target="_blank" rel="noopener nofollow">https://z.ai/subscribe?ic=C7UHL1TGWJ</a>

Kelvin Kwong

Zhipu AI's GLM-5 is a 744B parameter Mixture of Experts model that activates only 40B parameters per token, delivering strong coding performance that surpasses DeepSeek-V3.2 on SWE-bench and approaches Claude Opus 4.5. The model uses DeepSeek Sparse Attention for efficient inference and was trained on 28.5T tokens. It's best suited for developers needing offline coding capabilities with multi-GPU setups (8+ GPUs recommended for FP8), though cloud solutions may be more practical for those without the hardware.

GLM-5: How Zhipu AI Achieved Local Coding Excellence