# GLM-4.6: RAM and VRAM requirements

> GLM-4.6 is a 357B GLM model (Mixture-of-Experts, 32B active per token). At Q4_K_M it needs about **221.3 GB** to run and fits **0 of 40** tracked devices. Minimum to run: high-memory hardware.

Last validated: 2026-08-03. Sources: Ollama, HuggingFace GGUF repos, vendor specs.

## Memory by quantization
| Quant | On disk | To run (4k context) |
| --- | --- | --- |
| Q4_K_M | 216 GB | ~221.3 GB |
| Q8_0 | 379 GB | ~384.3 GB |
| FP16 | 714 GB | ~719.3 GB |

Memory = weights + KV cache + ~0.8 GB runtime overhead, and varies ±15% with context length.

## Will it run on my device?
- **Apple M1 (8GB)** (8 GB): No, not enough memory
- **iPhone 17** (8 GB): No, not enough memory
- **iPhone 17 Pro** (12 GB): No, not enough memory
- **16GB RAM Laptop (CPU/iGPU only)** (16 GB): No, not enough memory
- **Google Pixel 10 Pro** (16 GB): No, not enough memory
- **Nvidia GeForce RTX 3090 (24GB)** (24 GB): No, not enough memory
- **Apple M4 Pro (48GB)** (48 GB): No, not enough memory
- **Apple M3 Ultra (256GB)** (256 GB): No, not enough memory

Full table of all 40 devices: https://localmodel.run/model/glm-4.6

## How to run
Use LM Studio (Mac/Windows) or Ollama / vLLM (Linux).

## Details
- Parameters: 357B (MoE, 32B active per token)
- Default context: 200k tokens
- License: MIT (commercial use: yes)
- Released: 2025-08
- HuggingFace: 26,467 downloads/mo, 1229 likes

## FAQ
### How much VRAM or RAM does GLM-4.6 need?
About 221.3 GB at Q4_K_M (weights 216 GB + KV cache + overhead) at a 4k context. Budget ~384.3 GB for Q8_0.
### Can GLM-4.6 run on a laptop?
GLM-4.6 is large; you need a high-memory Mac or a 24 GB+ GPU at Q4_K_M.
### Can I use GLM-4.6 commercially?
Yes, MIT permits commercial use.

Sources: https://huggingface.co/zai-org/GLM-4.6, https://huggingface.co/unsloth/GLM-4.6-GGUF, https://unsloth.ai/docs/models/tutorials/glm-4.6-how-to-run-locally, https://github.com/ollama/ollama/issues/13391
More: https://localmodel.run/model/glm-4.6