GLM-Image
A solid pick for self-hosting and full data control.
Overview
GLM-Image, from Zhipu AI, pairs a 9B autoregressive generator with a diffusion decoder to handle both text-to-image and image-to-image tasks: editing, style transfer, identity-preserving generation. Its standout trait is accurate in-image text rendering and handling information-dense prompts, a known weak spot for pure diffusion models. Real limit: inference is still costly, roughly 23GB of GPU memory even with CPU offload enabled. Open weights under MIT licence allow fully on-premise deployment with data staying local, though its Chinese origin (Zhipu AI) warrants scrutiny for sensitive European use cases.
Skill profile
Not disclosed
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- you have GDPR constraints
Ideal use cases
- Marketing visuals with copy
- Image editing and style transfer
- Identity-preserving generation
- Information-dense graphic design
- On-premise creative pipelines
Access & availability
Key specifications
Privacy
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 5.1 GB | 8 GB | 16GB PC / M1+ Mac / 8GB GPU |
| Q8 · balanced | 9.6 GB | 13 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| FP16 · max quality | 18 GB | 22 GB | 32GB Mac / RTX 3090-4090 |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve zai-org/GLM-Image \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |