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Gemma-4-12B-OBLITERATED GPU Requirements: VRAM & Cheapest GPU

Gemma-4-12B-OBLITERATED has about 12.0B 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.

12.0BParameters
6.5 GBMin VRAM
$0.53/hrCheapest
< 2 minDeploy
OBLITERATUS/Gemma-4-12B-OBLITERATED
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12.0B paramstext-generationgemma4_unified8.1K downloads106 likesupdated Jun 9, 2026

To run Gemma-4-12B-OBLITERATED for inference at FP16, you need roughly 26 GB of VRAM. The cheapest fit on Spheron is L40S 48GB at about $0.61/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× L40S 48GBCHEAPEST
    Ada Lovelace · GDDR6
    $0.61/hr$0.61/hr
  • 02
    1× A100 80GB
    Ampere · HBM2e
    $0.82/hr$0.82/hr
  • 03
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $0.86/hr$0.86/hr
  • 04
    1× RTX 5090 32GB
    Blackwell · GDDR7
    $0.92/hr$0.92/hr
  • 05
    2× RTX 4090 24GB
    Ada Lovelace · GDDR6X
    $0.53/hr$1.06/hr

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

VRAM required to run Gemma-4-12B-OBLITERATED

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
FP1626 GB39 GB104 GB
INT813 GB20 GB52 GB
INT46.5 GB9.8 GB26 GB

Cheapest GPU to run Gemma-4-12B-OBLITERATED by precision

FP16
VRAM required26GB

Full precision. Best quality, highest memory.

Cheapest GPU
L40S 48GB
Ada Lovelace · GDDR6
$0.61/hr
L40S 48GB on Spheron
INT8
VRAM required13GB

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

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.53/hr
RTX 4090 24GB on Spheron
INT4
VRAM required6.5GB

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

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.53/hr
RTX 4090 24GB on Spheron

Inference vs fine-tuning Gemma-4-12B-OBLITERATED

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-4-12B-OBLITERATED, 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.

Deployment guideDeploy Gemma 4 step by stepHands-on production setup, GPU configs, and benchmarks for Gemma-4-12B-OBLITERATED.Read guide

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