EmbeddingGemma is our new best-in-class open embedding model designed for on-device AI. 📱 At just 308M parameters, it delivers state-of-the-art performance while being small and efficient enough to run anywhere - even without an internet connection.

🏆 Highest ranking on the MTEB benchmark - the gold standard for text embedding evaluation 🌐 Trained across 100+ languages 🛠️ Ready to go with @huggingface, @llama_index, @langchain and more. Here's how developers can get started with EmbeddingGemma →

The real breakthrough here is the "without an internet connection" part. On device processing is the key to making AI truly ubiquitous and private. The challenge will be maintaining this level of performance as the model is constrained by ever smaller hardware footprints.

Awesome performance and multilingual capabilities in 300M parameters. We have day zero support on Baseten for high-throughput, low-latency deployments:

Google needs better marketing team I guess. @demishassabis why don't you guys make any noise?

embedding models getting smaller is a huge win for edge ai. excited to see how developers leverage this offline capability for privacy and efficiency.

Love this direction. True intelligence won’t come just from more data or bigger models, but from systems that can exist, adapt, and want to survive in their environment. On-device models feel like the first step toward that.

Efficiency at this size isn’t just optimization, it’s what makes contextual AI deployable anywhere

It's amazing. Thank you!

Smaller models, bigger possibilities. Offline accessibility is a game changer for AI's future. This empowers edge computing in exciting new ways.

Deepmind is cooking

@demishassabis u mad about smol parameters bro? chad move = holding $TROLL 😈

when are u making a better battery life in the pixel by the way please fix that shit 😔😔😔😔😔

What kinds of apps could flourish once intelligence runs offline as easily as online?

always good to see your updates, Google 💌 following along with interest

Wow, that sleek on-device interface is a game-changer for AI accessibility! 🚀

AI that actually fits in your pocket.

👍👍👍

Love seeing models designed for on-device from the start. 308M params with best-in-class performance is the sweet spot for real-world edge deployment. Smaller, efficient models like this are the future. We're building the runtime layer for deploying them across iOS/Android/Mac/IoT at @RunAnywhereAI.

Edge AI win. Small size, big impact.

@grok i am new to ai tools, so can you tell me. What it actually is? What it actually do? What this post is? What this post talking about?

Amazing

what do you mean open?! Is it free?

Impressive model, compact yet powerful!

@grok is there similar available model ? Can i install it on my macbook pro ? Is it already pre-trained so i can discuss technical problem and brainstorm business ideas ? what are the top 3 in device model ?

Is there any benchmark that represents the performance between gemini embedding model and this gemma model and the openai embedding model

Impressive work. In Coral, SLM orchestration with small models has already outperformed Microsoft by 34% on the GAIA benchmark, showing how efficiency can beat scale in real tasks. ✅

On-device power.

nice

$oscr

308M parameters is impressive! Been testing it locally on my old laptop and it's surprisingly fast without sacrificing quality.

Did someone tried it on a macbook pro ? Is this the best embedding model ? I was looking for such LLM to work during travel (train, plane ect..) i became so dependant on Cursor, and other AI tools , would be great to have a small LLM for simple task (Brainsorm on code and ideas..)

Hey Siri

On-device AI getting this powerful at just 308M params is mind-blowing 🤯 huge step forward!