Intel / article

FastDeploy 2.0: A Large-Scale Model Inference and Deployment Toolkit with Native Support for ERNIE 4.5

Source: unknown
Date: Published Aug 14, 2025

Context

FastDeploy 2.0 addresses a concrete obstacle to running large models: fitting their stored weights into available memory. Baidu's deployment toolkit supports representing weights with fewer bits. For the 300-billion-parameter ERNIE 4.5 model, its technical announcement reports reducing weight storage from 600 GB to 89 GB, enabling deployment on a GPU with 141 GB of memory.

That compression has a measurable quality tradeoff. The article's comparison with four-bit weights shows lower scores for the two-bit version across its six listed tests, including instruction following. The useful result is a way to fit this model onto less hardware, with task-level needed to judge whether the compressed version remains suitable.

Terms in this piece · Glossary

eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

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