GLM-5.2 Requirements: VRAM, GPU & RAM by Quant (2026)

To run GLM-5.2 locally, plan on roughly 1.5TB of inference memory (VRAM + RAM combined) at full BF16 precision, and about 430GB at a practical 4-bit quantization. It is a 753B-total-parameter open-weight MoE; although only about 40B parameters are active per token, inference requires holding the weights of all experts in memory, so the required memory is determined by the total parameter count. The tables below are estimates that add inference overhead to the raw weight size, and they also vary with context length and batch size.

The table above is an approximation that adds roughly 15% inference overhead (KV cache, activations, etc.) to the model weight size. Using the full 1M-token context grows the KV cache further, so budget for more memory in real deployments. If you need specs for a lighter model, see the Gemma 4 Technical Report.

GLM-5.2 is an open-weight MoE (Mixture-of-Experts) large language model released by China's Z.ai (formerly Zhipu AI) on June 16, 2026. It has 753B total parameters, about 40B active parameters per token, and a context window of up to 1M input tokens and 131K output tokens. It is MIT-licensed, so it can be used freely including for commercial use. It scored 51 on Artificial Analysis' Intelligence Index v4.1, the highest of any open-weight model at release. On long-horizon coding benchmarks it reportedly surpasses GPT-5.5 in places and matches Claude Opus 4.8. Compared with the previous GLM-5.1, it strengthens stability for agent use and long-running tasks.

Quantization is a technique that compresses memory usage by reducing the number of bits used to represent a model's weights. Dropping BF16 (16-bit) to 4-bit cuts the weight size to about a quarter, greatly reducing required memory. The cost is lower numerical precision, but at around 4-bit the practical degradation is slight for many tasks. Compress further to 2-bit or 1-bit and output quality visibly drops, so you must either limit the use case or choose a higher bit depth.

A single typical consumer GPU in the 24–32GB class cannot hold GLM-5.2. Even the lightest 1-bit quantization needs about 180GB, so you need multiple GPUs bonded together or an environment with large unified memory. The realistic options are as follows.

On Apple Silicon, Metal is supported by default, and a strength is that Unified Memory can be treated as VRAM. However, GLM-5.2 needs about 430GB even at 4-bit, so full precision is impossible even on a single Mac Studio (up to roughly 512GB), and running it at all presupposes aggressive quantization. Clustering multiple units appears at the research stage, but stable operation is a high bar.

GLM-5.2 is supported by major high-throughput inference engines such as vLLM and SGLang, and once converted to GGUF format it can also be run quantized on llama.cpp or Ollama. When leveraging the 1M context for agent use, the vLLM/SGLang family is easier to work with because it balances KV-cache memory consumption against throughput.

Among open-weight MoE models, GLM-5.2 (753B total) is smaller than the 2.8T Kimi K3 and the 975B Inkling, which relatively lowers the bar for local execution. That said, needing hundreds of GB of memory puts it in the same "data-center class"; to run on a laptop, a small model like Gemma 4 is the realistic choice. It is important to choose based on both the level of intelligence your use case needs and the hardware you can provide.