Sold by: Waltsoft Inc.
Deployed on AWS
sentence-transformers/all-MiniLM-L6-v2 on SageMaker. 384-dimensional mean-pooled embeddings, 90MB model, 160M+ monthly downloads. Embed over 15,000 sentences per minute on ml.m5.xlarge at $0.07/hr.
Overview
all-MiniLM-L6-v2 from the Sentence Transformers project is one of the most downloaded embedding models on HuggingFace, with over 160 million monthly downloads. Fine-tuned on 1 billion sentence pairs from 20+ datasets including MS MARCO, NQ, and StackExchange, its 6-layer architecture compresses to 90MB while delivering strong general-purpose semantic similarity.
At 90MB model weight, it cold-starts in seconds and sustains over 15,000 short sentence embeddings per minute on a single ml.m5.xlarge CPU instance, making it the right choice for high-volume batch ingestion, real-time classification pipelines, and any workload where throughput matters more than top-1 retrieval precision. Note: input text is truncated at 256 tokens.
Deploy as a SageMaker endpoint in your own AWS account. Works with pgvector, Amazon OpenSearch, Pinecone, Weaviate, and any vector store that accepts 384-dimensional float32 vectors. Integrates directly with LangChain HuggingFaceEmbeddings.
Primary use cases: nightly re-embedding of large document corpora, real-time log and event clustering, semantic deduplication across high-volume feeds, chat history summarization for context windows, and cost-sensitive similarity search where extreme throughput is the constraint.
Highlights
- 90MB model, 6-layer architecture -- 15,000+ sentences per minute on ml.m5.xlarge, one of the fastest CPU embedding endpoints available
- 160M+ monthly downloads -- the most widely deployed open-source embedding model, with extensive LangChain, LlamaIndex, and vector store integrations
- Flat $0.07/hr, 384-dim mean-pooled output -- drop-in for pgvector, OpenSearch, Pinecone, and Weaviate, no per-token charges
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.07 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.07 |
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Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
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Inputs
- Summary
sentence-transformers/all-MiniLM-L6-v2 on SageMaker. 384-dimensional mean-pooled embeddings, 90MB model, 160M+ monthly downloads. Embed over 15,000 sentences per minute on ml.m5.xlarge at $0.07/hr.
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
Support
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Contact support@waltsoft.net for deployment assistance.
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