[Webinar] LLMs at Scale: Comparing Top Inference Optimization Libraries

Delve into the rapidly evolving field of LLM inference optimization and compare three prominent libraries – vLLM, TGI, and Infery-LLM, shedding light on why Infery-LLM emerges as the top contender.

This webinar is crucial for tech leads, project managers, software architects, and professionals in application development utilizing LLMs. It offers strategic understanding of LLM optimization techniques and tools, ensuring you’re equipped with the knowledge to make informed decisions for your projects.

Key takeaways:

  • LLM Library Features: Compare features and capabilities of leading LLM libraries.
  • Optimization Techniques: Learn about cutting-edge techniques like selective quantization, optimized beam search, continuous batching, and specialized kernels.
  • High-Volume, Real-Time User Interactions: Learn which LLM library best facilitates real-time customer engagement at high volumes.
  • Cost Efficiency: Explore the cost benefits and scalability enhancements with Infery-LLM. 

Whether you aim to deepen your knowledge in LLM inference optimization or seek effective solutions for AI projects, this webinar is your gateway to the future of LLM technology. Watch now!

If you want to learn more about optimizing your generative AI applications, book a demo here.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

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

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