GLM-5.1-FP8 GPU Requirements: VRAM & Cheapest GPU
GLM-5.1-FP8 has about 754B 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-5.1-FP8 for inference at FP16, you need roughly 1644 GB of VRAM. The cheapest fit on Spheron is 8x B300 288GB at about $26.32/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$3.29/hr$26.32/hr8× B300 288GBCHEAPESTBlackwell Ultra · HBM3e
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run GLM-5.1-FP8
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 | 1644 GB | 2465 GB | 6574 GB |
| INT8 | 822 GB | 1233 GB | 3287 GB |
| INT4 | 411 GB | 616 GB | 1644 GB |
Cheapest GPU to run GLM-5.1-FP8 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-5.1-FP8
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-5.1-FP8, 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.
Deployment guideDeploy GLM-5.1 step by stepHands-on production setup, GPU configs, and benchmarks for GLM-5.1-FP8.Read guideSimilar models
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