Inferact: Building the Infrastructure That Runs Modern AI

Video summary

Inferact’s vLLM founders aim to build an open-source inference layer for trillion-parameter models across 400,000–500,000 GPUs.

Inferact co-founders Simon Mo and Woosuk Kwon trace vLLM to a 2022 UC Berkeley effort to speed up Meta’s OPT model, which had 175 billion parameters. The project tackled language-model inference’s distinctive scheduling and memory demands, including variable-length requests and KV-cache management. Its community has grown beyond 2,000 GitHub contributors, with roughly 50 regular full-time contributors, and vLLM reportedly runs on 400,000–500,000 GPUs. The founders say inference is getting harder as models grow past a trillion parameters, hardware and architectures diversify, and AI agents create long-running, unpredictable tool interactions that complicate cache management. They describe Inferact as a company built to support vLLM and create a universal, open-source runtime for models, hardware, and applications. Its focus includes improving inference across large GPU clusters such as GB200 and GB300 systems.

Chapters

1. 0:00Introduction: Inferact’s universal inference layer and vLLM’s origins in Meta’s 2022 OPT model
2. 6:01Introduction: Dynamic LLM scheduling, Wau Kuan’s 2023 involvement, and an oversubscribed first vLLM meetup
3. 11:41Community and Collaboration in vLLM: 2,000-plus contributors, quarterly roadmaps, and million-dollar yearly CI costs
4. 19:19Understanding Inference Engines: API-to-KV-cache architecture and multi-trillion-parameter scaling challenges
5. 24:27Cluster Scale and GPU Deployment: 400,000–500,000 GPUs, divergent model architectures, and agent-driven KV-cache challenges
6. 31:19Belief in Open Source AI: vLLM powers Amazon Rufus and Character.AI’s hundreds of GPUs
7. 35:45Founding of Inferact: Yang joins as co-founder to advance vLLM and hiring
8. 40:00Future of Inference at Scale: GB200/GB300 NVL72 and a universal runtime

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