Sold by: LMCache LabÂ
Overview
LMCache lets LLMs prefill each text only once. By storing the KV caches of all reusable texts, LMCache can reuse the KV caches of any reused text (not necessarily prefix) in any serving engine instance. It thus reduces prefill delay, i.e., time to first token (TTFT), as well as saves the precious GPU cycles.
Highlights
- By combining LMCache with vLLM, LMCaches achieves 3-10x delay savings and GPU cycle reduction in many LLM use cases, including multi-round QA and RAG.
Details
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Delivery details
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:
Version release notes
- Use 0.1.4 LMCachedocker version
- Add max response token length selection
- Differentiate context from input
Additional details
Inputs
- Summary
- Use "input" to write your question.
- Use "type" to select turning on lmcache or not.
- Use "context" to write your context.
- Use "length" to select the maximum response token.
https://drive.google.com/file/d/1IHIUAugQKdKAZiPlZ7s3jF8GR1j04HXU/view?usp=drive_link
https://drive.google.com/file/d/1IHIUAugQKdKAZiPlZ7s3jF8GR1j04HXU/view?usp=drive_link
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Input text | The input should be a json containing "input", "context", "type", and "length". | Type: FreeText | Yes |
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