World Congress 2026 Europe • Jul 9, 2026 • Session details

Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated

Maximizing open-source LLM throughput requires aggressively balancing compute and memory bottlenecks. Master the complete GPU optimization stack, from simple model quantization to sophisticated speculative decoding.

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Matching moments

1:24 min

Exploring open source inference tools for language model serving

Legare Kerrison

Legare Kerrison +1 · World Congress 2026 North America

50 sec

Key takeaways for optimizing open source inference engine deployments

Legare Kerrison

Legare Kerrison +1 · World Congress 2026 North America

2:39 min

Open source tools for running and scaling models

Cedric Clyburn

Cedric Clyburn +1 · World Congress 2025

5:58 min

Architecting inference layers and meta harnesses to manage token costs

Hari Lingamagunta

Hari Lingamagunta +3 · World Congress 2026 North America

3:30 min

Optimizing performance using dedicated open source inference engines

Patrick Koss

Patrick Koss · World Congress 2025

1:47 min

Managing compute costs and AI model routing

Deivids Vilkinsons

Deivids Vilkinsons +3 · World Congress 2026 Europe

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Upcoming sessions on this topic

Starts in 12 days

Conference India 2026

November 25–26, 2026

Bengaluru, India

Get tickets All 107 sessions

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

Thu 26 Nov · Stage 2
Bypassing the GPU Tax: Smart Layer-7 Routing with Small Language Models (SLMs)
Sagar Utekar · CrowdStrike

Thu 26 Nov · Stage 2
Decision models for developers who use LLMs
Anurag Bhandari · antelligent

Thu 26 Nov · Stage 4

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