Model · Zhipu AI

GLM-5.2

753B MoE · Q4_K_M · 433.8 GB · open weights · curated for coding, agents, research · repo created 2026-06-16

Too much for any tracked machine: 448.3 GB at 8K context; the largest we track has 112.6 GB usable.

What the model needs: weights, context cache and runtime overhead at four reference contexts. The total is the model's own; what differs per machine is the usable memory it has to fit into — VRAM on a graphics card, unified memory on a Mac.

Cache shape from the model's config: 78 of 78 layers cache the full context with multi-head latent attention, one 576-dimensional latent per token at f16 in place of separate K and V.

All memory figures are estimates: measured quantized file size + computed context memory + runtime overhead, with a 12% safety margin on your hardware. Real usage varies with runtime version and settings.

Every tracked GPU and Mac at 8K and 32K context with a full-precision (f16) cache — the same engine as the finder and the GPU pages.

Runs on 0 of 32 tracked devices at 8K context, easily or with ordinary headroom.

GGUF is the quantized single-file format local runtimes load (Ollama, LM Studio, llama.cpp); our memory figures are measured from the linked GGUF file. The original repo holds full-precision safetensors — a much larger download meant for GPUs with far more memory.

More from Zhipu AI

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