Gemma-3-4B

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: 131K
Attention Type: Sliding Window Attention
VRAM requirements: 5.4 GB using 4 bits quantization
Developer: Google DeepMind
Transformers Version: 4.50.0.dev0
Ollama Version: 0.6
License: gemma

Public endpoint

Use our pre-built public endpoints to test inference and explore Gemma-3-4B capabilities.
Model Name Context Type GPU TPS Status Link
There are no public endpoints for this model yet.

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended configurations for hosting Gemma-3-4B

Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
rtx2080ti-1.16.32.160 16 32768 160 1 $0.41 Launch
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
rtx2080ti-1.16.32.160 16 32768 160 1 $0.41 Launch
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslat4-1.16.16.160 16 16384 160 1 $0.46 Launch
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx2080ti-2.12.64.160 12 65536 160 2 $0.69 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx3080-2.16.32.160 16 32762 160 2 $0.97 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch

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