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gemma-3-1b-it GPU Requirements: VRAM & Cheapest GPU

gemma-3-1b-it has about 1000M 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.

1000MParameters
0.5 GBMin VRAM
$0.65/hrCheapest
< 2 minDeploy
google/gemma-3-1b-it
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To run gemma-3-1b-it for inference at FP16, you need roughly 2.2 GB of VRAM. The cheapest fit on Spheron is RTX 4090 24GB at about $0.65/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× RTX 4090 24GBCHEAPEST
    Ada Lovelace · GDDR6X
    $0.65/hr$0.65/hr
  • 02
    1× A100 80GB
    Ampere · HBM2e
    $0.82/hr$0.82/hr
  • 03
    1× RTX 5090 32GB
    Blackwell · GDDR7
    $0.86/hr$0.86/hr
  • 04
    1× RTX PRO 6000 96GB
    Blackwell · GDDR7
    $0.91/hr$0.91/hr
  • 05
    1× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$0.96/hr

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

VRAM required to run gemma-3-1b-it

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
FP162.2 GB3.3 GB8.7 GB
INT81.1 GB1.6 GB4.4 GB
INT40.5 GB0.8 GB2.2 GB

Cheapest GPU to run gemma-3-1b-it by precision

FP16
VRAM required2.2GB

Full precision. Best quality, highest memory.

Cheapest GPU
RTX 4090 24GB
Ada Lovelace · GDDR6X
$0.65/hr
RTX 4090 24GB on Spheron
INT8
VRAM required1.1GB

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 required0.5GB

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-3-1b-it

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-3-1b-it, an on-demand RTX 4090 24GB 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 3 step by stepHands-on production setup, GPU configs, and benchmarks for gemma-3-1b-it.Read guide

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