Gen AI Models: Open Source vs Closed Source—Pros, Cons & Everything in Between

Resource Featured Image

Delivering high-performing generative AI applications hinges on having control over your models—from fine-tuning and optimization to deployment. Although convenient, closed-source models served via APIs provide little to no control over the model’s parameters or deployment. Open source models offer a path to enhanced flexibility. However, performance and customization of open source models can be an uphill battle. To unlock this potential fully, developers need tools specifically designed for the challenges of generative AI.

In this webinar, Yonatan Geifman, Co-Founder and CEO of Deci, navigates these complexities. Expect insights into advanced inference acceleration techniques, strategic deployment, and ways to optimize your workflow and enhance model performance. Broaden your generative AI expertise:

  • Gain a deep understanding of the generative AI inference stack and learn how to make informed decisions when selecting tools for optimal resource allocation and latency reduction.
  • Discover strategies to accelerate LLM inference, including efficient batching techniques, multi-GPU utilization, selective quantization, and hybrid compilation.
  • Become familiar with Deci’s high-performance SDK, designed to supercharge your models’ performance in on-premises deployments.

Fill out the form to access the webinar!

Access Webinar Now

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")