The Pulse
Simplismart Launches Inference Platform for NVIDIA Cloud Partners
Simplismart announced a white-label inference platform for NVIDIA Cloud Partners, designed to help manage and tune AI workloads on NVIDIA infrastructure. A separate company case study reports that optimizations for Invideo reduced serving a
AI.info Team · Written with AI · fact-checked ·
On February 23, 2026, Simplismart announced the general availability of its AI inference platform as a white-label offering for NVIDIA Cloud Partners. The company describes the platform as an orchestration layer on NVIDIA infrastructure, intended to help cloud providers and their customers build, tune and operate AI workloads.
The announcement says the software supports deployments using NVIDIA Inference Microservices, which cloud partners can offer as managed endpoints for applications such as voice agents, document processing and multimedia generation. Simplismart says its platform also supports workflow templates, scaling across deployment environments and onboarding new open-source models.
Managing different workload priorities
Simplismart presents the platform as a way to manage different performance needs across production AI applications. Some workloads prioritize low response times; others focus on processing volume or cost. The company says its orchestration tools let teams configure pipelines around latency, throughput and cost requirements, with options for cloud, on-premises and hybrid deployments.
“For example, a bank serving millions of daily customers using AI voice agents will be focused on quick response times.”
Amritanshu Jain, CEO and co-founder of Simplismart, gave that example in the announcement while describing how the priorities of AI applications can differ. He contrasted voice agents with document-processing workflows, where a bank might instead seek to process as many documents as possible at minimum cost.
The company says its platform is designed to support those varying objectives through endpoint management, workflow templates and automated scaling. Its announcement also describes support for governance, observability and performance controls across production environments. These are Simplismart’s descriptions of its offering; the announcement does not provide independent performance measurements for the platform.
Invideo case study reports lower costs
A separate Simplismart case study describes work with Invideo, an AI video-creation platform. Simplismart says it optimized parts of Invideo’s image and video pipeline using fused GPU kernels, caching, memory-efficient attention and changes to autoscaling and load balancing.
According to the case study, median latency fell from 20 seconds to 11 seconds, a 45% reduction, while serving and GPU costs for the image and video pipeline fell by 56%. Simplismart also reports that the time to move AI features from proof of concept into production decreased from roughly two weeks to three or four days.
The case study attributes the results to changes in model execution, memory use and workload management. It says caching reduced repeated computation, while memory-efficient attention reduced peak video memory use and allowed larger effective batch sizes at the same hardware tier. The reported results concern Invideo’s pipeline and do not establish that the same reductions would apply to other customers or workloads.
Together, the platform announcement and the Invideo example describe Simplismart’s focus on operating AI inference workloads: providing cloud partners with tools to deploy and manage services, and tuning execution for latency, throughput and cost. The company’s announcement identifies NVIDIA Cloud Partners as the intended users of the white-label offering, while its customer case study provides its own reported figures for one production deployment.