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World Congress 2024 • Aug 20, 2024 • Session details

Efficient deployment and inference of GPU-accelerated LLMs​

Adolf Hohl

Stop choosing between strict data privacy and deployment speed. Deploy optimized, GPU-accelerated LLMs securely to your infrastructure using NVIDIA NIM and LoRA adapters.

Efficient deployment and inference of GPU-accelerated LLMs​ thumbnail

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Matching moments

2:04 min

Optimizing and deploying containerized AI inference workloads

Ankit Patel Ankit Patel · World Congress 2024

3:10 min

Scaling customized inference models with NVIDIA NIM

Anshul Jindal Anshul Jindal · World Congress 2025

2:50 min

Executing LoRA fine-tuning using serverless Databricks AI runtimes

Viktoria Semaan Viktoria Semaan · World Congress 2026 Europe

5:58 min

Architecting inference layers and meta harnesses to manage token costs

Hari Lingamagunta Hari Lingamagunta +3 · World Congress 2026 North America

2:39 min

Open source tools for running and scaling models

Cedric Clyburn Cedric Clyburn +1 · World Congress 2025

1:12 min

Using vLLM recipes for optimal local hardware configuration

Legare Kerrison Legare Kerrison +1 · World Congress 2026 North America

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Upcoming sessions on this topic

Open session

How 8 GPUs Became 16: Rethinking LLM Inference Through Parallelism, Memory, and Runtime Design

  • Purnima Kumari

    DigitalOCean

    Sr. Solutions Architect II

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Bypassing the GPU Tax: Smart Layer-7 Routing with Small Language Models (SLMs)

  • Sagar Utekar

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Open session

Your AI Agents Are Bleeding Tokens: Building a Runtime Cost Control Plane for LLMs

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    Research Engineer

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    Leading AI Innovation

Open session

Engineering AI Strategy: Beyond the Hype

  • Omkar Srivastava

    ADSK

    Engineering Leader

Open session

Enterprise AI Memory at Scale

  • Aditya Joshi

    Walmart

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Decision models for developers who use LLMs

  • Anurag Bhandari

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