LLM
How to deploy GLM-4.7-Flash on a GPU cloud
A 31.2B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
GLM-4.7-Flash size and hardware requirements
31.2B
Total parameters
Mixture-of-experts: 4 of 64 experts active per token (exact active-parameter count not stated on the model card)
Active parameters
BF16
Published precision
69.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 58.2 GB | 69.8 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 29.1 GB | 34.9 GB | RTX 6000 Ada | 1 | $0.524/hr |
| INT4 (quantized) | 14.5 GB | 17.4 GB | RTX 3090 | 1 | $0.147/hr |
How to run GLM-4.7-Flash
Run GLM-4.7-Flash with vLLM
From zai-org/GLM-4.7-Flash's own deployment docs.
vllm serve zai-org/GLM-4.7-Flash \
--tensor-parallel-size 4 \
--speculative-config.method mtp \
--speculative-config.num_speculative_tokens 1 \
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-4.7-flashSource: https://huggingface.co/zai-org/GLM-4.7-Flash/raw/main/README.md
Run GLM-4.7-Flash with Ollama
Verified against Ollama's own library listing.
ollama run glm-4.7-flashRun GLM-4.7-Flash with GGUF quantizations
Prebuilt GGUF weights published at unsloth/GLM-4.7-Flash-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/GLM-4.7-Flash-GGUFDeploy GLM-4.7-Flash on Aquanode
Aquanode has no one-click deploy template for GLM-4.7-Flash; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod