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LatestGoogle DeepMind3-270m
Google DeepMindText / LLM

Google Releases Gemma 3 270M for On-Device AI

The new ultra-compact model from DeepMind is designed for efficient performance in resource-constrained environments like mobile and web.

Aug 5, 2025
NotableGemma
Gemma 3 270M

Google DeepMind has expanded its Gemma 3 family with a new, ultra-compact model: Gemma 3 270M. This latest release focuses on bringing capable language model performance to resource-constrained environments where computational power and memory are limited.

At just 270 million parameters, the model is significantly smaller than its multi-billion parameter counterparts. This small footprint makes it a strong candidate for running directly on devices like smartphones, laptops, and web browsers, enabling applications that can operate offline and without relying on cloud-based APIs.

Despite its size, Gemma 3 270M features a modern architecture and a substantial 32,768-token context window. Google positions the model as an ideal, cost-effective base for developers and researchers looking to experiment with fine-tuning for specialized tasks.

The model is available now on Hugging Face under the custom Gemma license. This release continues Google's strategy of providing a range of open models to cater to different scales of development, from large-scale research to local, privacy-preserving applications.

Sources

  • google/gemma-3-270m

    Hugging Face

    Visit
OlderOpenAI Releases 21B Open-Weight MoE ModelOpenAI · Reasoning · last yearNewerMeta Releases DINOv3 ViT-L/16 Vision BackboneMeta AI · Embeddings · last year

Get the model

Hugging Face

Specs

Parameters270M
Context window33K tokens
Size500 MB
PrecisionBF16
ArchitectureGemma3ForCausalLM
LicenseGEMMA
Downloads70.9K
Likes1.1K

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    Any modern laptop · 8 GB RAM

    1.7 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    1.5 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    1.4 GB
Your machine
GGUF builds
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM

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