gemma-3-4b-it

multimodal

Gemma 3 4B is a compact AI model developed by Google DeepMind, offering an excellent balance between size and functionality. Unlike the base 1B version, the 4B model supports multimodal inputs: text, images (with resolution up to 896x896 pixels), and short videos. For example, the model can recognize objects and text in images, such as extracting data from receipts or labels. To process images, it uses the SigLIP visual encoder, which automatically segments large files.

Its innovative architecture and efficient 5:1 ratio of local-to-global attention optimize memory usage while supporting a context window of up to 128K tokens. Gemma 3 4B supports 35 languages, including Russian. The model also includes function calling capabilities, enabling integration with APIs for task automation, such as generating SQL queries or transforming data.

This model is ideal for creating intelligent assistants and for fast document and image processing, making it a perfect choice for developers who need multimodal capabilities without requiring significant computational resources.


Announce Date: 12.03.2025
Parameters: 4B
Context: 132K
Layers: 34, using full attention: 5
Attention Type: Sliding Window Attention
Developer: Google DeepMind
Transformers Version: 4.50.0.dev0
License: gemma

Public endpoint

Use our pre-built public endpoints for free to test inference and explore gemma-3-4b-it capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to gemma-3-4b-it endpoints

There are no public endpoints for this model yet.

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint gemma-3-4b-it

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
131,072.0
1 $0.38 25.45 1.504 Launch
teslaa10-1.16.32.160
131,072.0
1 $0.53 77.80 3.860 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 119.26 4.176 Launch
rtx3080-2.16.32.160
131,072.0
tensor
2 $0.97 111.86 2.496 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 154.08 4.164 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 10.817 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 6.498 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 124.06 20.779 Launch
h100-1.16.64.160
131,072.0
1 $3.83 141.00 14.260 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 17.109 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 44.024 Launch
h200-1.16.128.160
131,072.0
1 $4.74 26.675 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 55.044 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
131,072.0
1 $0.38 32.81 2.036 Launch
teslaa10-1.16.32.160
131,072.0
1 $0.53 86.02 4.362 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 118.17 4.671 Launch
rtx3080-2.16.32.160
131,072.0
tensor
2 $0.97 2.969 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 134.30 4.715 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 11.287 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 7.049 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 152.63 21.255 Launch
h100-1.16.64.160
131,072.0
1 $3.83 191.74 14.044 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 238.39 16.890 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 44.494 Launch
h200-1.16.128.160
131,072.0
1 $4.74 27.053 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 55.367 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
131,072.0
1 $0.38 17.61 1.002 Launch
teslaa10-1.16.32.160
131,072.0
1 $0.53 38.95 3.357 Launch
rtx3090-1.16.24.160
131,072.0
1 $0.83 53.83 3.842 Launch
rtx3080-2.16.32.160
131,072.0
tensor
2 $0.97 1.809 Launch
rtx4090-1.16.32.160
131,072.0
1 $1.02 68.01 3.661 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 10.127 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 88.28 5.996 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 92.36 20.277 Launch
h100-1.16.64.160
131,072.0
1 $3.83 107.33 13.274 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 16.763 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 43.334 Launch
h200-1.16.128.160
131,072.0
1 $4.74 26.330 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 54.570 Launch

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