@PyTorch: How do we get more useful work—not just more tokens—from every AI dollar? This Wednesday at 11:50 AM at @AMD #Advancing…

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Summary

PyTorch Foundation CTO Matt White will speak at AMD's AdvancingAI event about optimizing AI inference economics using open-source tools like vLLM and SGLang, advocating for right-sized models and intelligent routing to improve dollar per intelligence.

How do we get more useful work—not just more tokens—from every AI dollar? This Wednesday at 11:50 AM at @AMD #AdvancingAI, PyTorch Foundation CTO @matthew_d_white will explore how open source, @vllm_project and @radixark’s @sgl_project are reshaping inference economics. In “Accelerating Open Source AI: Just Enough Intelligence,” Matt will outline a practical alternative to using frontier models for every task: → Decompose workflows → Select right-sized models → Route and cache intelligently → Escalate only when necessary → Measure cost per successfully completed task The goal is better Dollar per Intelligence: more correct, business-relevant outcomes from every dollar spent, with failures, retries, and review included in the calculation. Matt opens a series of talks in the AI Training & Inference track ahead of @simon_mo_, co-founder and CEO of @inferact and lead contributor to vLLM, and @ying11231, co-founder and CEO of RadixArk and co-creator of SGLang. Moscone West, San Francisco Wednesday, July 22 11:50 AM PDT Explore the track:
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How do we get more useful work—not just more tokens—from every AI dollar? This Wednesday at 11:50 AM at @AMD #AdvancingAI, PyTorch Foundation CTO @matthew_d_white will explore how open source, @vllm_project and @radixark’s @sgl_project are reshaping inference economics.

In “Accelerating Open Source AI: Just Enough Intelligence,” Matt will outline a practical alternative to using frontier models for every task: → Decompose workflows → Select right-sized models → Route and cache intelligently → Escalate only when necessary → Measure cost per successfully completed task

The goal is better Dollar per Intelligence: more correct, business-relevant outcomes from every dollar spent, with failures, retries, and review included in the calculation.

Matt opens a series of talks in the AI Training & Inference track ahead of @simon_mo_, co-founder and CEO of @inferact and lead contributor to vLLM, and @ying11231, co-founder and CEO of RadixArk and co-creator of SGLang.

Moscone West, San Francisco Wednesday, July 22 11:50 AM PDT

Explore the track:

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