What GPU do I need to run zai-org/GLM-4.7-Flash?
A 31.2B (MoE) language model for chat and instruction-following. 31.2B parameters, published in BF16. View on Hugging Face
GLM-4.7-Flash is published by zai-org on Hugging Face, with 1,948,824 downloads and 1,831 likes to date. It's a Glm4MoeLiteForCausalLM model built for text-generation, published natively in BF16.
What GLM-4.7-Flash is
GLM-4.7-Flash is a 31.2B-parameter mixture-of-experts language model published by Z.ai (Zhipu) on Hugging Face. It is released under MIT.
License note: permissive: allows commercial use, modification and redistribution. Facts in this section are sourced from GLM-4.7-Flash's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 58.2 GB | 69.8 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 29.1 GB | 34.9 GB | RTX 4090 | 1 | $0.485/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| INT4 (quantized) | 14.5 GB | 17.4 GB | RTX A5000 | 1 | $0.176/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run GLM-4.7-Flash at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
GLM-4.7-Flash: common questions
Can GLM-4.7-Flash run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 69.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
What is the least VRAM GLM-4.7-Flash can run in?
17.4 GB, at INT4 (quantized), which fits a 24 GB card, against 69.8 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing GLM-4.7-Flash lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More GLM-4.5 models
- GLM-4.7-Flash (31.2B, BF16)
- GLM-4.6V-Flash (10.3B, BF16)
- GLM-4.5V (107.7B, BF16)
- GLM-4.5-Air (110.5B, BF16)
- GLM-4.5-Air (110.5B, BF16)
Related reading: A100 pricing and specs, and The best GPUs for AI, ranked.
