01What it can do
- Ultra-compact (0.5 GB VRAM Q4)
- Specialized in document OCR
- 128k native context
- Runs on CPU and iGPU
- —Not general-purpose (OCR only)
- —Gated model on Hugging Face
- —Capabilities limited by the 0.9B size
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for GLM-OCR 1.1B?
To run GLM-OCR 1.1B locally with Q4 quantization, you need about 0.6 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.
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On the go: GLM-OCR 1.1B also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.