What GPU do I need to run Efficient-Large-Model/gemma-2-2b-it?
2.6B parameters, published in BF16. View on Hugging Face
Set up gemma-2-2b-itgemma-2-2b-it is published by Efficient-Large-Model on Hugging Face, with 151,875 downloads and 3 likes to date. It's a Gemma2ForCausalLM model built for text-generation, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 4.9 GB | 5.8 GB | RTX 3070 | 1 | $0.088/hr |
| FP8 (quantized) | 2.4 GB | 2.9 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 1.2 GB | 1.5 GB | RTX 3070 | 1 | $0.088/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run gemma-2-2b-it at its published (BF16) precision: 1× RTX 3070, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-2-2b-it: common questions
How much VRAM does gemma-2-2b-it need?
5.8 GB at BF16, 2.9 GB at FP8 (quantized), 1.5 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 4.9 GB of weights plus inference overhead is the whole requirement.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Efficient-Large-Model models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: RTX 3070 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
