diffusiongemma-26B-A4B-it-NVFP4 GPU Requirements: VRAM & Cheapest GPU
diffusiongemma-26B-A4B-it-NVFP4 has about 14.4B 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 diffusiongemma-26B-A4B-it-NVFP4 for inference at FP16, you need roughly 31 GB of VRAM. The cheapest fit on Spheron is L40S 48GB at about $0.67/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$0.67/hr$0.67/hr1× L40S 48GBCHEAPESTAda Lovelace · GDDR6
- 02$0.79/hr$0.79/hr1× A100 80GBAmpere · HBM2e
- 03$0.86/hr$0.86/hr1× RTX PRO 6000 96GBBlackwell · GDDR7
- 04$0.86/hr$0.86/hr1× RTX 5090 32GBBlackwell · GDDR7
- 05$0.53/hr$1.06/hr2× RTX 4090 24GBAda Lovelace · GDDR6X
Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.
VRAM required to run diffusiongemma-26B-A4B-it-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 | 31 GB | 47 GB | 126 GB |
| INT8 | 16 GB | 24 GB | 63 GB |
| INT4 | 7.9 GB | 12 GB | 31 GB |
Cheapest GPU to run diffusiongemma-26B-A4B-it-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 diffusiongemma-26B-A4B-it-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 diffusiongemma-26B-A4B-it-NVFP4, an on-demand L40S 48GB 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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