Nvidia has invested in Bengaluru-based AI inference startup Simplismart, joining a $14 million Series B round that values the four-year-old company at roughly $100 million post-money. The all-primary round was led by Dallas Venture Capital, with Accel, Shastra VC and Micromax also participating.

The deal is a notable vote of confidence in an Indian company working on one of enterprise AI’s most expensive problems: running models in production at a speed and cost that actually make business sense.

Inference tuned to the workload, not the benchmark

As organisations pour money into GPUs, many are discovering that serving AI models reliably at scale is harder than training or piloting them. Each workload carries its own trade-offs between latency, throughput and compute cost, and getting that balance right usually demands painstaking configuration work.

Simplismart’s software sits between AI models and the infrastructure they run on, automatically adjusting how models are deployed and managed to hit a customer’s targets for response time, request volume and compute consumption. The company says this lets enterprises squeeze more out of the GPUs they already own.

“Nobody actually needs the fastest or the cheapest inference,” co-founder and CEO Amritanshu Jain told TechCrunch, arguing that companies instead need models that run at the speed and cost appropriate to their specific application.

Customers can run models on Simplismart’s own platform or inside their existing cloud and on-premises environments, with tools for fine-tuning, autoscaling and performance monitoring. Cloud providers can also use the software to build their own inference services on GPU infrastructure.

Customers and traction

• AI video platform InVideo cut its GPU serving costs by 56% using Simplismart, according to the company.
• Other customers include AI video players HeyGen and Higgsfield, and enterprise AI startup Ema.
• Indian cloud provider Yotta uses Simplismart’s technology to power serverless AI inference within its Shakti Studio platform.
• Annualised revenue run rate has grown about 14-fold in six months to an eight-figure sum, based on recent usage, the company said.
• More than half of revenue comes from the US, even though most customers are in India.

Why Nvidia is backing software that saves GPUs

On the surface, the world’s dominant AI chip supplier funding software that can reduce GPU requirements looks paradoxical. Jain’s view is that more efficient inference makes Nvidia hardware more attractive and broadens AI adoption overall. Beyond the investment, Nvidia’s solutions architects have been working with Simplismart engineers on technical integrations, including around Nvidia’s Dynamo inference software, and the chipmaker has helped introduce the startup to potential customers and partners.

The inference-optimisation market is getting crowded, with offerings from Nvidia itself, hyperscalers such as AWS, Google Cloud and Microsoft Azure, and a growing pack of startups. Simplismart says it wins where customers face strict service-level and total-cost-of-ownership requirements.

What’s next

Founded in 2022 by former Oracle engineer Amritanshu Jain and former Google engineer Devansh Ghatak, who met at BITS Pilani, Simplismart has 58 employees. It plans to use the new capital to expand in the US, grow its team and deepen its inference software. Jain said the Series B is closed and will not be extended, and declined to comment on an earlier media report that the company was in talks for a larger round.

For Indian CIOs, the round underlines a shift in the AI conversation from access to compute towards making that compute pay for itself, a theme that will only sharpen as GPU constraints persist and AI workloads move from pilots into production.

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