GLM-5.2-NVFP4 GPU Requirements: VRAM & Cheapest GPU
GLM-5.2-NVFP4 has about 381B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
To run GLM-5.2-NVFP4 for inference at FP16, you need roughly 831 GB of VRAM. The cheapest fit on Spheron is 4x B300 288GB at about $14.00/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
- 01$3.50/hr$14.00/hr4× B300 288GBCHEAPESTBlackwell Ultra · HBM3e
- 02$2.74/hr$21.92/hr8× B200 192GBBlackwell · HBM3e
- 03$3.31/hr$26.48/hr8× H200 141GBHopper · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run GLM-5.2-NVFP4
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
| Precision | Inference | LoRA fine-tune | Full fine-tune |
|---|---|---|---|
| FP16 | 831 GB | 1246 GB | 3322 GB |
| INT8 | 415 GB | 623 GB | 1661 GB |
| INT4 | 208 GB | 311 GB | 831 GB |
Cheapest GPU to run GLM-5.2-NVFP4 by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning GLM-5.2-NVFP4
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. For GLM-5.2-NVFP4, an on-demand B300 288GB instance covers inference and LoRA, while a full fine-tune needs several times that memory and often spans multiple GPUs. Check the live GPU pricing for current rates.
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