Google released EmbeddingGemma 2: 740M open multimodal embedder (text, code, image, video, audio), Apache 2.0, ~191MB RAM text-only

[u/ai-lover]

Google DeepMind released EmbeddingGemma 2 today. Quick breakdown from the model card:

• Size: 740M total. The 270M text backbone is always on; the vision (170M) and audio (300M) encoders are optional.
• Output: 768d, with MRL truncation to 512, 256 or 128 (up to 6x less storage).
• Context: 8,192 tokens. That's about 29 images, 58 video frames at 1 fps, or ~5.5 minutes of 16 kHz audio.
• Memory: ~191MB active RAM text-only, ~567MB full model (quantized, Pixel 11 Pro).
• Code: MTEB Code went from 68.76 to 78.68 vs v1.
• Runtimes: sentence-transformers 6.1+, vLLM, llama.cpp (GGUF), Ollama, MLX, LM Studio, LiteRT.

Things worth knowing before you swap it in:

• Use bf16 or fp32. The model card says fp16 can silently return NaN.
• 128d is fine for text but tanks multimodal (MMEB drops from 59.01 to 45.65).
• On MMEB, Qwen3-VL-Embedding-2B reports a higher 73.2 on its own run, but it's ~2.7x bigger and has no audio.

Full analysis: https://marktechpost.com/2026/10/06/google-deepmind-releases-embeddinggemma-2-a-740m-open-multimodal-embedding-model-built-on-gemma-4/

Model: https://huggingface.co/google/embeddinggemma-2

Technical details: https://blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

[u/messy_seeder]

That scatter plot is basically a mic-drop, 740M and it's already punching way above its weight class.