# GLM-4.7-Flash: RAM and VRAM requirements

> GLM-4.7-Flash is a 30B GLM model (Mixture-of-Experts, 3B active per token). At Q4_K_M it needs about **19.2 GB** to run and fits **13 of 40** tracked devices. Minimum to run: Nvidia GeForce RTX 4090 (24GB).

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 | 17.05 GB | ~19.2 GB |
| Q8_0 | 29.66 GB | ~31.8 GB |
| FP16 | 55.79 GB | ~57.9 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): Yes, it runs
- **Apple M4 Pro (48GB)** (48 GB): Yes, it runs (room for Q8_0)
- **Apple M3 Ultra (256GB)** (256 GB): Yes, it runs (room for FP16)

Full table of all 40 devices: https://localmodel.run/model/glm-4.7-flash

## How to run
Quickest path: `ollama run glm-4.7-flash:latest`. On Mac, LM Studio (ships MLX) is fastest; on Linux, Ollama for chat or vLLM to serve; on Windows, LM Studio or Ollama.

## Details
- Parameters: 30B (MoE, 3B active per token)
- Default context: 200k tokens
- License: MIT (commercial use: yes)
- Released: 2026-01
- HuggingFace: 2,074,814 downloads/mo, 1800 likes

## FAQ
### How much VRAM or RAM does GLM-4.7-Flash need?
About 19.2 GB at Q4_K_M (weights 17.05 GB + KV cache + overhead) at a 4k context. Budget ~31.8 GB for Q8_0.
### Can GLM-4.7-Flash run on a laptop?
GLM-4.7-Flash is large; you need a high-memory Mac or a 24 GB+ GPU at Q4_K_M.
### Can I use GLM-4.7-Flash commercially?
Yes, MIT permits commercial use.

Sources: https://huggingface.co/zai-org/GLM-4.7-Flash, https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF, https://ollama.com/library/glm-4.7-flash/tags
More: https://localmodel.run/model/glm-4.7-flash