LMCache and llm_efficiency
LMCache provides a production-ready KV cache optimization system for any LLM, while llm_efficiency is an educational implementation demonstrating KV cache concepts within a minimal GPT architecture—making them educational reference vs. practical tool rather than true competitors or complements.
About LMCache
LMCache/LMCache
Supercharge Your LLM with the Fastest KV Cache Layer
When you're running Large Language Models (LLMs) and notice slow responses or high computing costs, especially with long prompts or repeated questions, LMCache can help. It takes your LLM inputs and stores parts of them so the LLM doesn't have to re-process the same information, significantly speeding up response times and reducing GPU usage. This is ideal for infrastructure engineers, MLOps specialists, or anyone managing LLM deployment who wants to optimize performance and cost.
About llm_efficiency
dataflowr/llm_efficiency
KV Cache & LoRA for minGPT
This project helps developers working with Large Language Models (LLMs) to make their models run faster and fine-tune more efficiently. It provides implementations of KV Caching to speed up how LLMs generate text, and LoRA (Low-Rank Adaptation) to reduce the cost of adapting pre-trained models to new tasks. If you're building or customizing LLMs, you can use these techniques to optimize performance and resource use.
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