Author(s): Youssef Hosni

Originally published on Towards AI.

Combining layers in transformer models makes them bigger and better at understanding language tasks. But making these big models costs a lot to train and they need a lot of memory and computer power to use afterward.

The most popular Large Language Models (LLM) today such as ChatGpt have billions of settings and sometimes they have to handle long pieces of text, which makes them even more expensive to use.

For example, RAG pipelines require putting large amounts of information into the input of the model, greatly increasing the amount of processing work the LLM has to do.

In the article, you will be provided with a comprehensive list of resources to delve into the foremost challenges encountered in LLM inference and proffer practical solutions.

Understanding LLM Inference Optimization 1.1. Mastering LLM Techniques: Inference Optimization by Nvidia1.2. LLM Inference by DatabricksDeep Understanding of LLM Inference Optimization 2.1. Deep Dive: Optimizing LLM inferenceLLM Inference by Hugging Face3.1. GPU Inference by Hugging Face3.2. Optimizing LLMs for Speed and Memory by Hugging Face3.3. Assisted Generation by Hugging FaceLLM Inference Optimization Libraries & Tools4.1. LLMs at Scale: Comparing Top Inference Optimization Libraries4.2. Large language model inference optimizations on AMD GPUs4.3. Accelerate Large Language Model (LLM) Inference on Your… Read the full blog for free on Medium.

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Published via Towards AI