embeddinggemma-2
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve google/embeddinggemma-2New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
embeddinggemma-2 is an open-weight text embedding model from google, published on Hugging Face in September 2026. It has 740M parameters and is released under the apache-2.0 license.
HardwareLink to this section
At full FP16 precision the weights need about 1 GB of memory; 8-bit quantization brings that to roughly 1 GB. That is small enough for most consumer GPUs, Apple Silicon Macs, and many CPUs.
PopularityLink to this section
embeddinggemma-2 had 503,609 downloads on Hugging Face in the last month and has 3,349 likes.
Run it locallyLink to this section
The weights are on Hugging Face and load with Hugging Face Transformers. Follow the model card for the exact setup and license terms.
Frequently asked
Quick answers to common questions
How much VRAM does embeddinggemma-2 need?
embeddinggemma-2 with 0.74B parameters needs approximately 1 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is embeddinggemma-2 better than other google models?
embeddinggemma-2 has 0.74B parameters with 262,144 context - a strong choice for embeddings.
What license is embeddinggemma-2 under?
embeddinggemma-2 is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs embeddinggemma-2 well?
With 0.74B parameters, embeddinggemma-2 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for embeddinggemma-2?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~1 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can embeddinggemma-2's context window handle?
embeddinggemma-2 supports a 262,144-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with embeddinggemma-2?
embeddinggemma-2 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec