What GPU do I need to run google/gemma-3-4b-it?
A 4.3B vision-language model that reads images alongside text. 4.3B parameters, published in BF16. View on Hugging FaceGated
gemma-3-4b-it is published by google on Hugging Face, with 1,526,070 downloads and 1,466 likes to date. It's a Gemma3ForConditionalGeneration model built for image-text-to-text, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
What gemma-3-4b-it is
gemma-3-4b-it is a 4.3B-parameter vision-language model published by Google on Hugging Face. It is released under Gemma Terms of Use.
License note: Google's own terms, not OSI-approved open source; permits commercial and research use under conditions in the license text, and the repo is gated on Hugging Face. Facts in this section are sourced from gemma-3-4b-it's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Image understanding and captioning
- Visual question answering
- Document/OCR-style reading
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 | 8.0 GB | 9.6 GB | RTX 3060 | 1 | $0.110/hr |
| FP8 (quantized) | 4.0 GB | 4.8 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 2.0 GB | 2.4 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-3-4b-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-3-4b-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: 139,264 bytes at 16-bit while the context is inside the window.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.19 GB |
| 32K | 0.74 GB |
| 128K | 2.6 GB |
Computed from the layer, head and window counts in the model's own configuration (unsloth/gemma-3-4b-it), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
gemma-3-4b-it: common questions
Does gemma-3-4b-it fit on a 12 GB GPU?
Yes. At BF16 it needs 9.6 GB of VRAM, so a 12 GB card holds it with 2.4 GB to spare. An 8 GB card is not enough for it at BF16.
Do I need approval to download gemma-3-4b-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 9.6 GB the model needs once you have them.
What is the least VRAM gemma-3-4b-it can run in?
2.4 GB, at INT4 (quantized), which fits a 6 GB card, against 9.6 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
How to run gemma-3-4b-it
Run gemma-3-4b-it with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve google/gemma-3-4b-it --tensor-parallel-size 1Run gemma-3-4b-it with Ollama
Verified against Ollama's own library listing.
ollama run gemma3:4bRun gemma-3-4b-it with GGUF quantizations
Prebuilt GGUF weights published at unsloth/gemma-3-4b-it-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/gemma-3-4b-it-GGUFDeploy gemma-3-4b-it on Aquanode
Aquanode has no one-click deploy template for gemma-3-4b-it; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 3060 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Gemma 3 models
- gemma-3-12b-it (12.2B, BF16)
- gemma-3-12b-it-heretic-v2 (12.2B, BF16)
- gemma-3-12b-it-heretic (12.2B, BF16)
- gemma-3-27b-it (27.4B, BF16)
- gemma-3-27b-it-FP8-dynamic (27.4B, F8_E4M3)
Alternatives at this size
Other models for image-text-to-text within about a third of gemma-3-4b-it's 4.3B parameters, from other model lines.
- Qwen3.5-4B (4.7B, BF16)
- Unlimited-OCR (3.3B, BF16)
- chandra-ocr-2 (5.3B, BF16)
- DeepSeek-OCR (3.3B, BF16)
- Phi-3.5-vision-instruct (4.1B, BF16)
More on gemma-3-4b-it
Fits on a 12 GB GPU at BF16: every model that fits in 12 GB.
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Best vision-language models: how gemma-3-4b-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.
