Embeddinggemma 300M
Developed by Google, EmbeddingGemma 300M is an open-source embedding model that excels at producing vector representations of text, making it well-suited for search and retrieval tasks such as classification, clustering, and semantic similarity search. With a context window of 2,048 tokens, this model is notable for its ability to be deployed in resource-limited environments due to its relatively small size.
Input
Output
Context
2K
Max Output
-
Parameters
302.9M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Embeddinggemma 300M
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Embeddinggemma 300M
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Embeddinggemma 300M advertises a context window of 2,048 tokens. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Embeddinggemma 300M is an open-weight model released under the Gemma license, so the weights can be downloaded and self-hosted within that license's terms.
Embeddinggemma 300M accepts Text and produces Embedding. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Embeddinggemma 300M is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Embeddinggemma 300M is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.7 GB at INT8 for the weights and a default context, from the catalog's 302.9M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 13, 2026
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