Gemma 4 family lands on Amazon Bedrock
AWS has made Google DeepMind's Gemma 4 family of open-weight models available on Amazon Bedrock, offering three instruction-tuned variants with built-in reasoning, native function calling, and multimodal input.
Score breakdown
Gemma 4's availability on Bedrock gives developers managed access to Apache 2.0-licensed open-weight models with native function calling and multimodal support across dense and MoE architectures.
- 01Gemma 4 is now available on Amazon Bedrock, announced by Aris Tsakpinis on the AWS AI Blog.
- 02The family was built by Google DeepMind and released under the Apache 2.0 license.
- 03Three instruction-tuned variants are offered: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B.
Amazon Bedrock is now hosting the Gemma 4 family of open-weight models, developed by Google DeepMind and made available under the Apache 2.0 license. The family is designed with a focus on intelligence-per-parameter, aiming to cover a broad range of deployment scenarios with efficient model architectures.
In MoE models, only a fraction of the model's parameters activate per request, enabling more efficient inference.
The three instruction-tuned variants — Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B — span both dense and mixture-of-experts (MoE) architectures. In MoE models, only a fraction of the model's parameters activate per request, enabling more efficient inference. All variants include built-in reasoning, native function calling, and multimodal input support across text and image.
Key facts
- 01Gemma 4 is now available on Amazon Bedrock, announced by Aris Tsakpinis on the AWS AI Blog.
- 02The family was built by Google DeepMind and released under the Apache 2.0 license.
- 03Three instruction-tuned variants are offered: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B.
- 04The family covers both dense and mixture-of-experts (MoE) architectures.
- 05MoE models activate only a fraction of parameters per request.
- 06All variants include built-in reasoning, native function calling, and multimodal input across text and image.
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