BAAIBGE Base En V1.5
Bge Base En V1.5 is an English text embedding model developed by BAAI, designed for dense retrieval tasks to support retrieval-augmented language models. It is part of the broader FlagEmbedding project focused on retrieval-augmented LLMs.
Input
Output
Context
512
Max Output
n/a
Parameters
109.5M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for BGE Base En V1.5
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 BGE Base En V1.5
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
BGE Base En V1.5 advertises a context window of 512 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. BGE Base En V1.5 is an open-weight model released under the MIT license, so the weights can be downloaded and self-hosted within that license's terms.
BGE Base En V1.5 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 BGE Base En V1.5 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; BGE Base En V1.5 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 0.5 GB at INT8 for the weights and a default context, from the catalog's 109.5M 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 7, 2026
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