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Model reference · open weights

gemma-2

LLMs unsloth Text gen 2 builds Open, with conditions 43k dl/mo

gemma-2 is an open-weight language model from unsloth. gemma-2-2b-it (BF16) weighs 5.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.

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  • Gemma-2 is a 27.2B parameter text-generation model published by unsloth as unsloth/gemma-2-27b.
  • It supports English with a context length of 8192 tokens and is distributed under the gemma licence.
  • The model card describes it as a directly quantized 4bit version designed for efficient finetuning.

Summary of the unsloth/gemma-2-27b model card, 2026-10-01. The estimate below is for another build of the family.

Hugging Face ↗

What it is

Released byunsloth
Released2024-06-27
Parameters27.2B
VRAM5.2 GB for the weights

What it runs on

Memory and cards for gemma-2-2b-it (BF16)

5.2 GBweights, file size
53 MBcache per 1K tokens
436 MBwindow cache per request
805 MBruntime overhead, at least
8,192 tokenscontext max
CardRequests at onceContext maxMemory
8K each
RTX 3060 12 GB6all 8K11.6 GB
RTX 4060 Ti 16 GB10all 8K15.4 GB
RTX 3090 24 GB19all 8K23.4 GB
RTX 4090 24 GB19all 8K23.4 GB
RTX 5090 32 GB28all 8K31.0 GB
L40S 48 GB43all 8K44.0 GB
A100 80 GB82all 8K78.2 GB
H100 80 GB76all 8K78.1 GB
RTX PRO 6000 Blackwell 96 GB94all 8K93.8 GB
DGX Spark (GB10) 128 GB unified110all 8K107 GB
H200 141 GB145all 8K138 GB
B200 180 GB188all 8K176 GB
2× RTX 3060 12 GB
tensor parallel		18all 8K11.6 GB a card
Memory needed at each load
Requests at once8K tokens each
16.9 GB
510.4 GB
813.0 GB
1620.0 GB
3234.0 GB
6461.9 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
gemma-2-27b ↗ 27.2BBF16 54.5 GBH100 80 GB
gemma-2-2b-it (above) ↗ 2.6BBF16 5.2 GBRTX 3060 12 GB

From the model card

What unsloth says about gemma-2

Read the model card

Reminder to use the dev version Transformers:

pip install git+https://github.com/huggingface/transformers.git

Finetune Gemma, Llama 3, Mistral 2-5x faster with 70% less memory via Unsloth!

Directly quantized 4bit model with bitsandbytes.

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

  • * Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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