GLM-5.2 VRAM Requirements
Developed by Z.ai (Zhipu AI)
Find out exactly how much VRAM you need to run GLM-5.2 locally. Calculate the memory footprint of different GGUF quantization variants (like Q4_K_M or Q8_0), estimate your context length KV cache VRAM footprint, and determine if your hardware supports a full GPU VRAM offload or if you will need to rely on slow partial CPU offloading to avoid a CUDA Out of Memory (OOM) error.
Hardware Configuration
Adjust settings to check compatibility with your system in real time.
Available: 14.50 GB
Available: 29.00 GB
This configuration exceeds your system's usable memory capacity. Attempting to run it will cause crashes or freeze your machine.
Requires 1516.18 GB total memory (weights: 1403.19 GB, context overhead: 0.69 GB, activation overhead: 112.31 GB).
GLM-5.2 Quantization Formats & VRAM Compatibility
Select a format to set it as active and calculate your system fit dynamically.
| Quant | Weights | KV Cache | Overhead | Total VRAM | Status | Links |
|---|---|---|---|---|---|---|
| Base (Unquantized) | 1403.19 GB | 0.69 GB | 112.31 GB | 1516.18 GB | Too Large | HF weights |
GLM-5.2 KV Cache Memory Breakdown
How to Setup and Run GLM-5.2 Locally
Method A: Ollama (Recommended)
Ollama is the easiest way to run models in the background. First, download it from ollama.com, then execute this terminal command:
ollama run <model-name>Method B: LM Studio (GUI)
If you prefer a full graphical interface with chat UI and local server hosting:
- Download and install LM Studio.
- Search for GLM-5.2 in the home page search tab.
- Select a quantization level (like Q4_K_M) that fits your VRAM, click download, and load it to chat.
Model Specs
- DeveloperZ.ai (Zhipu AI)
- Parameter Count753B
- Base File Size1403.2 GB
- AvailabilityAPI & Local
- Input ModalitiesText
- Official SiteVisit site
Standard Benchmark Scores
Commercial API Pricing
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