MODEL · GPU GUIDE

gemma-3-27b-it GPU Requirements: VRAM & Cheapest GPU

gemma-3-27b-it has about 27.4B 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.

27.4BParameters
15 GBMin VRAM
$0.53/hrCheapest
< 2 minDeploy
google/gemma-3-27b-it
VIEW ON HUGGINGFACE ↗
27.4B paramsimage-text-to-textgemma31.5M downloads2.0K likesupdated Mar 21, 2025gated · request access on HF

To run gemma-3-27b-it for inference at FP16, you need roughly 60 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
    $1.32/hr$1.32/hr
  • 03
    2× RTX 5090 32GB
    Blackwell · GDDR7
    $0.68/hr$1.36/hr
  • 04
    1× GH200 96GB
    Grace Hopper · HBM3
    $1.88/hr$1.88/hr
  • 05
    2× L40S 48GB
    Ada Lovelace · GDDR6
    $0.96/hr$1.92/hr

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

VRAM required to run gemma-3-27b-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
FP1660 GB90 GB239 GB
INT830 GB45 GB120 GB
INT415 GB22 GB60 GB

Cheapest GPU to run gemma-3-27b-it by precision

FP16
VRAM required60GB

Full precision. Best quality, highest memory.

Cheapest GPU
A100 80GB
Ampere · HBM2e
$0.82/hr
A100 80GB on Spheron
INT8
VRAM required30GB

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

Cheapest GPU
RTX 5090 32GB
Blackwell · GDDR7
$0.68/hr
RTX 5090 32GB on Spheron
INT4
VRAM required15GB

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-3-27b-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-3-27b-it, 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 3 step by stepHands-on production setup, GPU configs, and benchmarks for gemma-3-27b-it.Read guide

Similar models

Compare GPU requirements for models in the same class.

FAQ / 05

gemma-3-27b-it GPU questions