What GPU do I need to run google/gemma-3-270m-it?
268M parameters, published in BF16. View on Hugging FaceGated
Set up gemma-3-270m-itgemma-3-270m-it is published by google on Hugging Face, with 92,924 downloads and 615 likes to date. It's a Gemma3ForCausalLM model built for text-generation, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 0.5 GB | 0.6 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 0.2 GB | 0.3 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.1 GB | 0.1 GB | RTX 3060 | 1 | $0.110/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-3-270m-it at its published (BF16) precision: 1× RTX 3060, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
gemma-3-270m-it: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Sliding-window attention: windowed layers stop growing at the window, the full-attention layers keep growing. Cached per token: 18,432 bytes at 16-bit while the context is inside the window.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.02 GB |
| 32K | 0.10 GB |
Computed from the layer, head and window counts in the model's own configuration (unsloth/gemma-3-270m-it), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
gemma-3-270m-it: common questions
How much VRAM does gemma-3-270m-it need?
0.6 GB at BF16, 0.3 GB at FP8 (quantized), 0.1 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 0.5 GB of weights plus inference overhead is the whole requirement.
Do I need approval to download gemma-3-270m-it?
Yes. google gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 0.6 GB the model needs once you have them.
How many copies of gemma-3-270m-it fit on one RTX 3060?
20, by VRAM alone. That card carries 12.0 GB and one copy needs 0.6 GB at BF16, on a live rate of $0.110/hr for the whole card. Throughput is not modelled here, so 20 copies is not 20 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 3 models
- gemma-3-270m (268M, BF16)
- gemma-3-1b-it (1000M, BF16)
- gemma-3-1b-pt (1000M, BF16)
- gemma-3-4B-T1-it (3.9B, BF16)
- gemma-3-4b-it (4.3B, BF16)
Lineage
Based on gemma-3-270m.
Also published as: unsloth/gemma-3-270m-it. Identical copies of this model under another account.
Alternatives at this size
Other models for text-generation within about a third of gemma-3-270m-it's 268M parameters, from other model lines.
- pythia-160m (213M, F16)
- SmolLM2-360M (362M, BF16)
- gpt2-medium (380M, F32)
- LFM2.5-350M (354M, BF16)
- Lite-Oute-1-300M (300M, F32)
More on gemma-3-270m-it
Fits on an 8 GB GPU at BF16: every model that fits in 8 GB.
Best chat and assistants models: how gemma-3-270m-it ranks against the rest.
Related reading: H100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
