How much VRAM does GLM-5.2 744B (MoE) need at Q4_K_M? Zhipu 1M MLA frontier
GLM-5.2 744B at Q4_K_M is a 643 GB resident deployment for 1M context. Active-only only gets you to 209 GB because the 78-layer MLA cache at full context is the binding constraint, not the weights.
GLM-5.2 744B at Q4_K_M with native 1M context needs about 643 GB of VRAM with all experts resident. Zhipu's successor to GLM-5.1 activates 40B parameters per token from the same 744B total pool but rebuilds the attention to 78 MLA layers interleaved with DSA IndexShare every 4 layers. Active-expert offload drops the resident footprint to roughly 209 GB, though the key-value cache at full context is the binding constraint rather than the weights.
By TechCompare · Updated
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Estimated VRAM required
643 GB
744B params at Q4_K_M, 1,048,576 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Hardware that fits
No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.
How this is calculated
GLM-5.2 keeps the 744B parameter pool from GLM-5.1 but upgrades the attention: 78 MLA layers compressed to a 512-dim latent with 1 effective KV head, plus DSA IndexShare every 4 layers for sequence sparsity. At Q4_K_M the full parameter pool is 417 GB of weights. The MLA key-value cache at the full 1M context window uses a significant 167 GB. Activation and software overhead adds about 58 GB. When you offload cold experts and keep only the active 40B weights in VRAM, weight memory shrinks to 22 GB, but the 167 GB key-value cache stays the same. That drops total usage to 209 GB, with the cache accounting for most of it.
Verdict
Resident mode needs eight 80GB datacenter cards with NVLink or five 141GB H200 cards. Active-only offload fits on dual 80GB cards, but the 1M-context key-value cache dominates. Capping context is the fastest way to shrink the footprint. For most workloads the official Zhipu API is the economical starting point.
More GLM scenarios
Frequently asked questions
Why does active-only offload barely help GLM-5.2 at 1M context?
How is GLM-5.2 different from GLM-5.1?
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