Nvidia has backed Indian startup Simplismart in a $14 million funding round, betting on software that helps companies run AI models more efficiently and with fewer GPUs. As the AI boom strains computing capacity, businesses face heavy spending on hardware to power applications, while running those models reliably and economically at scale remains a severe challenge. Different workloads demand distinct trade-offs between speed, cost, and performance, requiring complex adjustments to deploy models according to business requirements. Simplismart has raised a $14 million Series B round, valuing the four-year-old startup at about $100 million. Dallas Venture Capital led the Series B investment, with participation from Nvidia, Accel, Shastra VC, and Micromax.
Market realities answer through financial pressure why creators of the primary hardware for artificial intelligence finance software that allows companies to spend less money on buying that very hardware. Companies are burning capital expenditures on expensive graphics processors to deploy models, yet inference economics at scale become unsustainable without deep software optimization.
Software Infrastructure and Cost Optimization
Simplismart’s software sits between AI models and the computing infrastructure they run on, adjusting how models are deployed and managed to meet customer requirements. It optimizes factors such as response speed, request handling volume, and computing power consumption, helping companies extract more value from their existing GPU stack. The Bengaluru-based startup offers companies a way to deploy and run AI models through its own platform or within their existing cloud and on-premises infrastructure, while also providing tools to fine-tune models, automatically scale computing resources, and monitor performance. Cloud providers also use this technology to build and operate their own AI inference services on GPU infrastructure.
Simplismart states that its software helped AI video-generation platform InVideo cut its GPU serving costs by 56%. The startup also works with AI video companies HeyGen and Higgsfield, as well as enterprise AI startup Ema, while Indian cloud provider Yotta uses the technology to power the serverless AI inference service within its Shakti Studio platform. Simplismart’s annualized revenue run rate has grown 14-fold over the past six months to an eight-figure sum. More than half of Simplismart’s revenue comes from the U.S., despite the startup having more customers in India.
As Amritanshu Jain, co-founder and CEO of Simplismart, explained in an interview, nobody actually needs the fastest or the cheapest inference. Companies instead need to run AI models at speeds and costs that make sense for their specific applications. Where the startup wins is where people must be more stringent on their service-level agreements and total cost of ownership. Jain noted that unlike rivals offering preset options for faster or cheaper inference, Simplismart aims to find a balance tailored to each customer’s needs.
Market Competition and Strategic Integration
Nvidia has been working with Simplismart on technical integrations, including around its Dynamo inference software. Founded in 2022 by former Oracle engineer Amritanshu Jain and former Google engineer Devansh Ghatak, the startup now employs 58 people according to Amritanshu Jain. Simplismart plans to use the new funding to expand its presence in the U.S., grow its team, and invest further in developing its inference software.
The startup serves customers in an increasingly crowded market for AI inference software, competing with offerings from Nvidia itself, as well as major cloud providers and hyperscalers including Amazon Web Services, Google Cloud, and Microsoft Azure. A growing number of startups are developing tools to make AI models faster and cheaper to run, driven by rising demand for AI applications and the need to make better use of expensive GPU resources. The investment presents an apparent paradox as Nvidia, the dominant supplier of AI chips, is backing software that can help companies run some workloads with fewer GPUs.
Amritanshu Jain argued that making AI inference more efficient could ultimately benefit Nvidia by making its hardware more attractive to customers and encouraging wider adoption of AI applications. Instead of delivering hardware efficiency that would instantly solve enterprise capacity crunches, the market has settled on a software layer that forces buyers to carefully calculate every single inference cycle while hardware shipments continue at record pace.
Editorial analysis
Why would the dominant chipmaker fund tools designed to help enterprises run models on fewer processors? By joining the $14 million round for India-based Simplismart, Nvidia is backing software that optimizes model deployment and lowers hardware consumption. Earlier we reported on Nvidia joining a $500 million round for Recursive Superintelligence alongside GV, and in late August we wrote that Nvidia spent $6 billion to license Poolside's platform while taking on 109 of its engineers. Backing software that cuts hardware demand answers the very question of deployment costs by keeping model serving financially viable for enterprise clients.
We covered this: April 19, August 21