This article explains how to accurately quantize a Large Language Model (LLM) and convert it to the GGUF format for efficient CPU inference. It covers using an importance matrix (imatrix) and K-Quantization method with Gemma 2 Instruct as an example, while highlighting its applicability to other models like Qwen2, Llama 3, and Phi-3.
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The author explores the use of Gemma 2 and Mozilla's llamafile on AWS Lambda for serverless AI inference
Explore the best LLM inference engines and servers available to deploy and serve LLMs in production, including vLLM, TensorRT-LLM, Triton Inference Server, RayLLM with RayServe, and HuggingFace Text Generation Inference.
Podman AI Lab is the easiest way to work with Large Language Models (LLMs) on your local developer workstation. It provides a catalog of recipes, a curated list of open source models, experiment and compare the models, get ahead of the curve and take your development to new heights wth Podman AI Lab!
