01What it can do
- Apache 2.0 (free for commercial use)
- Multimodal: text, images, and audio in the same vector space
- Ultra-light: ~0.4 GB VRAM in Q4, ~0.9 GB RAM on CPU
- Official Ollama tag: one-command installation
- —Embedding model — does not generate text (not an LLM chat model)
- —Context limited to 8k tokens: split long documents
- —To integrate into a complete RAG pipeline (vector database + generator LLM)
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for EmbeddingGemma 2?
To run EmbeddingGemma 2 locally with Q4 quantization, you need about 0.4 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.
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On the go: EmbeddingGemma 2 also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Lifetime updates
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.