GLM-5 Memory Requirements Explained: MLA + DeepSeek Sparse Attention (DSA)

2026-02-16 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

Zhipu AI has released GLM 5, a successor to GLM 4.7, increasing its parameter count from 355B to 744B. A key innovation in GLM 5 is the integration of DeepSeek Sparse Attention (DSA) with Multi-Head Latent Attention (MLA) to accelerate inference, particularly with long context windows. This article explains the benefits of DSA, details the new features in GLM 5, and analyzes its practical operational requirements. It also examines the hardware specifications needed to run the model and compares the memory consumption across various quantized versions, providing insights into the model's efficiency and deployment considerations.

Key takeaway

For AI Architects and MLOps Engineers evaluating large language models for long-context applications, GLM 5's integration of DeepSeek Sparse Attention (DSA) with MLA offers significant inference speed improvements. You should assess the memory requirements of its 744B parameters and explore the available quantized variants to optimize deployment on your existing hardware, potentially reducing operational costs and latency for demanding workloads.

Key insights

GLM 5 integrates DeepSeek Sparse Attention with MLA for faster long-context inference, scaling to 744B parameters.

Principles

• Sparse attention improves long-context inference speed.
• Quantization reduces memory footprint for large models.

Method

GLM 5 combines Multi-Head Latent Attention (MLA) with DeepSeek Sparse Attention (DSA) to optimize inference performance, especially for extended context lengths.

In practice

• Evaluate DSA for long-context inference tasks.
• Compare quantized GLM 5 variants for deployment.

Topics

• GLM-5
• DeepSeek Sparse Attention
• Multi-Head Latent Attention
• Large Language Models
• Inference Optimization

Best for: AI Architect, MLOps Engineer, NLP Engineer, AI Engineer, Machine Learning Engineer, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.