Latency optimization

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean

This talk argues against the common practice of picking LLMs based solely on leaderboards, emphasizing that there's no single best model, only the right one for a given request. It introduces Digital Ocean's Inference Router, a customizable, open-source solution that intelligently selects models based on user-defined preferences (cost, latency, task, quality) rather than benchmarks, demonstrating significant cost savings and performance improvements in live demos.

Decagon’s Playbook for Building Enterprise AI Applications

Decagon’s Playbook for Building Enterprise AI Applications

Jesse Zhang and Ashwin Sreenivas, co-founders of Decagon, discuss their company's transition to open-source models for enterprise AI, emphasizing how fine-tuned small models outperform frontier models on specific tasks. They delve into the role of application-layer companies in an AI-first world, their product-driven 'glass box' approach for enterprises, and the transformative power of their 'Duet Autopilot' agent, which builds other AI agents. The conversation also covers AI's impact on jobs, highlighting the Jevons Paradox in customer support.

Serving Voice AI at $1/hr: Open-source, LoRAs, Latency, Load Balancing - Neil Dwyer, Gabber

Serving Voice AI at $1/hr: Open-source, LoRAs, Latency, Load Balancing - Neil Dwyer, Gabber

An in-depth look at Gabber's experience deploying the Orpheus text-to-speech model to production, covering latency optimization, high-fidelity LoRa-based voice cloning, and a cost-effective inference stack using vLLM and a consistent hash ring for load balancing.