How much VRAM does GLM-5.3 755B (MoE) need at Q4_K_M? Zhipu's latest 1M flagship
GLM-5.3 755B at Q4_K_M is a ~649 GB resident deployment for 1M context, with 423 GB of it in weights. For most buyers the Z.ai API beats self-hosting until volume hits the hundreds of millions of tokens per month.
GLM-5.3 755B at Q4_K_M with native 1M context needs about 649 GB of VRAM with all experts resident. Z.ai's August 2026 flagship keeps the 753B-scale pool of GLM-5.2 and the same 40B active per-token budget, with the MLA attention stack carried over from 5.2. Those 40B active params don't shrink what has to stay loaded, because different tokens pick different experts.
By TechCompare · Updated
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Estimated VRAM required
649 GB
755B 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 or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Hardware that fits
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.
How this is calculated
The 755B pool at Q4_K_M is 423 GB of weights. The MLA key-value cache across 78 layers at a 512-dim latent is 168 GB at the full 1M context. Activation and runtime overhead adds about 59 GB. That makes GLM-5.3 nearly identical to GLM-5.2 on footprint at the same context, since the cache layout is unchanged.
Verdict
The 649 GB resident total is 423 GB of Q4 weights, 168 GB of MLA KV cache at 1M, and 59 GB of overhead. Resident mode needs eight 80 GB cards or five 141 GB H200 cards with NVLink. The 40B active count doesn't lower the resident total, because any token can route to any expert. Capping context to 256K cuts the cache to ~42 GB. Z.ai's official API pricing sits in the mid-tier band, so self-hosting at this scale only wins on data residency or burst-proof capacity.
More GLM scenarios
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Frequently asked questions
How does GLM-5.3 differ from GLM-5.2 on VRAM?
Can I run GLM-5.3 on two 80 GB GPUs?
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