Clearly explained guide for running quantized open-source LLM applications on CPUs using Llama 2, C Transformers, GGML, and LangChain
Third-party commercial large language model (LLM) providers like OpenAI’s GPT4 have democratized LLM use via simple API calls. However, teams may still require self-managed or private deployment for model inference within enterprise perimeters due to various reasons around data privacy and compliance.
The proliferation of open-source LLMs has fortunately opened up a vast range of options for us, thus reducing our reliance on these third-party providers.
When we host open-source models locally on-premise or in the cloud, the dedicated compute capacity becomes a key consideration. While GPU instances may seem the most convenient choice, the costs can easily spiral out of control.
In this easy-to-follow guide, we will discover how to run quantized versions of open-source LLMs on local CPU inference for retrieval-augmented generation (aka document Q&A) in Python. In particular, we will leverage the latest, highly-performant Llama 2 chat model in this project.
The accompanying GitHub repo for this article can be found here.
LLMs have demonstrated excellent capabilities but are known to be compute- and memory-intensive. To manage their downside, we can use quantization to compress these models to reduce the memory footprint and accelerate computational inference while maintaining model performance.
Quantization is the technique of reducing the number of bits used to represent a number or value. In the context of LLMs, it involves reducing the precision of the model’s parameters by storing the weights in lower-precision data types.
Since it reduces model size, quantization is beneficial for deploying models on resource-constrained devices like CPUs or embedded systems.