What GPU do I need to run zai-org/GLM-5.1?
A 754B (MoE) language model for chat and instruction-following. 753.9B parameters, published in BF16. View on Hugging Face
GLM-5.1 is published by zai-org on Hugging Face, with 87,580 downloads and 1,841 likes to date. It's a GlmMoeDsaForCausalLM model built for text-generation, published natively in BF16.
What GLM-5.1 is
GLM-5.1 is a 754B-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-5.1'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 | 1404.2 GB | 1685.0 GB | No capable live offer found | – | – |
| FP8 (quantized) | 702.1 GB | 842.5 GB | No capable live offer found | – | – |
| INT4 (quantized) | 351.0 GB | 421.3 GB | RTX PRO 6000 | 5 | $6.88/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.
GLM-5.1: common questions
Can GLM-5.1 run on a single GPU?
Not on a desktop card. At BF16 it needs 1685.0 GB of VRAM, more than a single 32 GB desktop card holds. No card currently listed on the marketplace both supports BF16 and has enough VRAM for it, so how many it would take is not something this page can answer today.
How to run GLM-5.1
Run GLM-5.1 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve zai-org/GLM-5.1 --tensor-parallel-size 1Run GLM-5.1 with GGUF quantizations
Prebuilt GGUF weights published at unsloth/GLM-5.1-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/GLM-5.1-GGUFDeploy GLM-5.1 on Aquanode
Aquanode has no one-click deploy template for GLM-5.1; 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 (1685 GB VRAM or more).
- 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-5 models
- GLM-5 (753.9B, BF16)
- GLM-5-FP8 (753.9B, F8_E4M3)
- GLM-5.1-FP8 (753.9B, F8_E4M3)
- GLM-5.2-FP8 (753.3B, F8_E4M3)
- GLM-5.2 (753.3B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
