Inferact Raises $150M at $800M Valuation for AI Inference Tech

The Inference Gold Rush Intensifies

Artificial intelligence investment is rapidly refocusing on the critical stage of inference, the process of deploying trained models into practical applications. This shift is drawing substantial capital, with the AI inference market projected to expand significantly, potentially reaching over $250 billion by 2030 with a compound annual growth rate around 19%. A parallel forecast estimates the market at $103.73 billion in 2025, growing to $312.64 billion by 2034. This surge is fueled by advancements in generative AI and large language models, prompting enterprises to prioritize real-time deployment and hyperscalers to bolster infrastructure supporting compute-intensive operations. The operational costs and efficiency of running these models during inference represent a key bottleneck, making optimization technologies highly attractive to investors.

Inferact’s Strategic Ascent

Inferact, the newly formed commercial entity backed by the creators of the open-source vLLM project, has successfully closed a $150 million seed funding round, achieving a valuation of $800 million. The round was co-led by prominent venture capital firms Andreessen Horowitz (a16z) and Lightspeed Venture Partners, signaling strong investor confidence in the inference optimization space. Additional investment came from firms including Sequoia Capital, Altimeter Capital, Redpoint Ventures, and ZhenFund. Inferact’s core mission is to enhance AI inference by reducing operational expenses and improving model stability and speed, building upon the foundation of its widely adopted open-source vLLM engine. The vLLM project itself originated from the UC Berkeley lab of Ion Stoica, a co-founder of Databricks, and is now managed under the PyTorch Foundation, indicating a continued commitment to the open-source community.

Competitive Currents in AI Infrastructure

The commercialization of vLLM into Inferact mirrors a trend seen with other successful open-source AI projects. Notably, the SGLang project has spun out into RadixArk, securing a valuation of approximately $400 million in a round led by Accel. These developments underscore a highly competitive environment where specialized solutions for AI deployment are rapidly attracting venture capital. Beyond startups, major technology players are also aggressively pursuing inference optimization. Amazon Web Services (AWS), for instance, is leveraging its custom AI chips like Inferentia and Trainium to significantly lower inference costs and latency, positioning itself against established hardware providers such as NVIDIA. The broader AI server market is also experiencing robust growth, with shipments expected to increase by over 28% year-over-year in 2026, driven by increased AI infrastructure investment from cloud service providers. Venture capital firms like Lightspeed Venture Partners continue to pour capital into the AI sector, having deployed over $5.5 billion across more than 165 AI-native companies, signaling sustained investor appetite for cutting-edge AI technologies.