Red Hat talk on enterprise agentic inference at PyTorch Conference
The gap between pilot demos and always-on enterprise serving is now being closed inside the PyTorch and vLLM ecosystems, meaning teams no longer need proprietary stacks for reliable agentic workloads. Platform engineers and ML infrastructure leads evaluating serving stacks can benchmark vLLM's enterprise features against alternatives. Developers of independent tools can also learn how Ecosystem Landscape membership raises their project's visibility.
Joseph Groenenboom of Red Hat will present at PyTorch Conference North America in San Jose on 20-21 October.
The talk covers productionising agentic inference with PyTorch and vLLM for 24/7 enterprise systems.Topics include reliability, observability, KV cache management, concurrency, tool calling support and long context multi-turn chat.
The session, led by the PyTorch Foundation Ecosystem Working Group, also explains the Ecosystem Landscape and its GitHub-based application process for independent projects.
Curious about how to make enterprise agentic inference production-ready with @PyTorch and @vllm_project & to learn more about the PyTorch Landscape? Join Joseph Groenenboom of @RedHat at PyTorch Conference North America.
While serving AI models for research and pilot use cases is a well solved problem, moving it to 24/7 Enterprise ready systems is the next important phase in AI maturity. The reliability, observability, KV cache management, and concurrency requirements for Enterprise readiness are non-trivial problems. Rising to this challenge, PyTorch, vLLM, and the other foundation projects and broader ecosystem have begun adding Enterprise level features and enhancements. This talk will cover a sampling of some of the project work; from core PyTorch project build infrastructure up to model serving improvements to account for tool calling support and long context multi-turn chat.
Led by the PyTorch Foundation Ecosystem Working Group, you will learn what the Ecosystem Landscape is and why it matters: how membership drives visibility and community engagement for independent projects; how to apply: a lightweight, GitHub-based process that makes it straightforward for projects to apply for ecosystem status; and how lifecycle management works: what ongoing membership looks like and how the Working Group supports active projects.
See you at #PyTorchCon NA in San Jose, October 20-21! Register at: https://t.co/jBApW8nESi
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include PyTorch, vLLM, agents, enterprise AI.
Source attribution: curated from PyTorch (@PyTorch).
