Nvidia backs Simplismart in $14M round to expand AI inference and cloud partnerships

Nvidia backs Simplismart in $14M round to expand AI inference and cloud partnerships

The AI infrastructure startup helps enterprises deploy and operate AI models while optimizing computing costs and performance.

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by Diego Almada Lopez

Bengaluru-based Simplismart secured $14 million in Series B funding to scale its AI inference infrastructure software, as the company reported a 14-fold increase in annualized revenue run rate over six months, according to a Tuesday announcement.

Dallas Venture Capital led the round, which also included Nvidia, Accel, Shastra VC and Micromax. Customers running production workloads on its platform include HeyGen, Higgsfield, InVideo, Shiprocket, Apna and Ema, across video, voice, multimodal and enterprise AI applications.

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Founded in 2022 by Amritanshu Jain, a former Oracle engineer, and Devansh Ghatak, a former Google engineer, Simplismart develops AI infrastructure software designed to help enterprises run AI models more efficiently and at lower costs.

The startup offers an inference and MLOps platform for deploying and managing AI workloads across cloud and on-premises infrastructure.

The platform supports language, vision, speech and image-generation models, with tools for optimizing computing resources, managing deployments, fine-tuning models and monitoring performance. Simplismart offers shared and dedicated endpoints as well as private-cloud and on-premises options, allowing companies to tailor deployments to their security, performance and cost requirements.

Simplismart plans to use the funding to improve its inference engine and orchestration platform and expand partnerships with cloud providers, particularly in North America.

The firm is also developing a dynamic scheduling layer to match workloads with suitable GPU capacity across regions and time zones. The system is aimed at addressing mismatches between available computing resources and customer demand while preserving performance requirements and data-residency restrictions.

Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
TECHNOLOGY
Nvidia backs Simplismart in $14M round to expand AI inference and cloud partnerships
Nvidia backs Simplismart in $14M round to expand AI inference and cloud partnerships

The AI infrastructure startup helps enterprises deploy and operate AI models while optimizing computing costs and performance.

by Diego Almada Lopez

Share

Add us on Google

Bengaluru-based Simplismart secured $14 million in Series B funding to scale its AI inference infrastructure software, as the company reported a 14-fold increase in annualized revenue run rate over six months, according to a Tuesday announcement.

Dallas Venture Capital led the round, which also included Nvidia, Accel, Shastra VC and Micromax. Customers running production workloads on its platform include HeyGen, Higgsfield, InVideo, Shiprocket, Apna and Ema, across video, voice, multimodal and enterprise AI applications.

Advertisement

Founded in 2022 by Amritanshu Jain, a former Oracle engineer, and Devansh Ghatak, a former Google engineer, Simplismart develops AI infrastructure software designed to help enterprises run AI models more efficiently and at lower costs.

The startup offers an inference and MLOps platform for deploying and managing AI workloads across cloud and on-premises infrastructure.

The platform supports language, vision, speech and image-generation models, with tools for optimizing computing resources, managing deployments, fine-tuning models and monitoring performance. Simplismart offers shared and dedicated endpoints as well as private-cloud and on-premises options, allowing companies to tailor deployments to their security, performance and cost requirements.

Simplismart plans to use the funding to improve its inference engine and orchestration platform and expand partnerships with cloud providers, particularly in North America.

The firm is also developing a dynamic scheduling layer to match workloads with suitable GPU capacity across regions and time zones. The system is aimed at addressing mismatches between available computing resources and customer demand while preserving performance requirements and data-residency restrictions.

Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.