GTEdgeAI/gt-ai-os-community
3
0
Fork 0
Code Issues Packages Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
tbendien
2025-12-29 20:11:49 -05:00
committed by GitHub
parent 012b978c21
commit fd285bdd94
1 changed files with 10 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
README.md
View File

@@ -40,18 +40,19 @@ Carefully choose the correct installation script for your host.
## Embedding model GPU acceleration: ## Embedding model GPU acceleration:
NVIDIA GPU's and Apple Silicon will significantly accelerate embedding acceleration (uploading files and documents for Retrieval Augemented Generation "RAG"). NVIDIA GPU's and Apple Silicon will significantly accelerate embedding acceleration (uploading files and documents for Retrieval Augemented Generation "RAG").
Ensure that your NVIDIA GPU hardware is installed prior to starting the GT AI OS installation. As of release 2.0.34 the minimum GPU VRAM needed at installation time is 4GB as the embedding model installed is teh BAAI/bge-m3 which consumes around 3.78GB once fully loaded onto the GPU.
There are no aditional drivers or dependencies for using Apple Silicon to accelerate the embedding model that is part of the standard installation. We will be adjusting the installation scripts in future release so that smaller GPU's down to 1GB can be used on mini desktop computers.
At v2.0.33, once you install GT AI OS, you cannot install GPU hardware and switch from CPU to GPU for embeddings. Ensure that your NVIDIA GPU hardware is physically installed prior to starting the GT AI OS installation.
Note that all NVIDIA drivers and dependencies will be installed during the standard Ubuntu runbook.
There are no aditional drivers or dependencies needed for using Apple Silicon to accelerate the embedding model as that is part of the standard installation.
At v2.0.34, once you install GT AI OS, you cannot install GPU hardware and switch from CPU to GPU for embeddings.
We are looking to fix this in a future release. We are looking to fix this in a future release.
NVIDIA drivers and dependencies and tools will be installed during the pre requisites part of the runbook. If you do not have an NVIDIA GPU installed in your host, then the CPU and host RAM will be used for running the embedding model.
CPU vs GPU accelerated embedding will result in slower file uploads when adding files to datasets.
If you do not have an NVIDIA GPU in your target install host, then the CPU will be used for running the embedding model.
CPU vs GPU accelerated embedding will exhibit slow file uploads when adding files to datasets
Embedding model is installed by default.
--- ---