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Gemma-4-31B-IT-NVFP4 GPU Requirements: VRAM & Cheapest GPU

Gemma-4-31B-IT-NVFP4 has about 20.9B 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.

20.9BParameters
11 GBMin VRAM
$0.65/hrCheapest
< 2 minDeploy
nvidia/Gemma-4-31B-IT-NVFP4
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20.9B paramstext-generation2.4M downloads502 likesupdated May 8, 2026

To run Gemma-4-31B-IT-NVFP4 for inference at FP16, you need roughly 45 GB of VRAM. The cheapest fit on Spheron is A100 80GB at about $0.82/hr. Quantize to INT4 to run it on a smaller, cheaper GPU.

GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k

Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.

RANKCONFIGURATIONPER GPUTOTAL $/HR
  • 01
    1× A100 80GBCHEAPEST
    Ampere · HBM2e
    $0.82/hr$0.82/hr
  • 02
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $0.91/hr$0.91/hr
  • 03
    1× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$0.96/hr
  • 04
    2× RTX 4090 24GB
    Ada Lovelace · GDDR6X
    $0.65/hr$1.30/hr
  • 05
    1× H100 80GB
    Hopper · HBM3
    $1.49/hr$1.49/hr

Live pricing aggregated from 5+ data center partners. Per-minute billing, no commitments.

VRAM required to run Gemma-4-31B-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.

PrecisionInferenceLoRA fine-tuneFull fine-tune
FP1645 GB68 GB182 GB
INT823 GB34 GB91 GB
INT411 GB17 GB45 GB

Cheapest GPU to run Gemma-4-31B-IT-NVFP4 by precision

FP16
VRAM required45GB

Full precision. Best quality, highest memory.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT8
VRAM required23GB

8-bit quantized. ~2x smaller, minimal quality loss.

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.65/hr
RTX 4090 24GB on Spheron
INT4
VRAM required11GB

4-bit quantized. ~4x smaller, runs on smaller GPUs.

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.65/hr
RTX 4090 24GB on Spheron

Inference vs fine-tuning Gemma-4-31B-IT-NVFP4

InferenceWeights + KV cache
LoRA fine-tune~1.5×+ low-rank adapter
Full fine-tune~4×+ gradients + optimizer state

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 Gemma-4-31B-IT-NVFP4, an on-demand A100 80GB 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 Gemma 4 step by stepHands-on production setup, GPU configs, and benchmarks for Gemma-4-31B-IT-NVFP4.Read guide

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