由于AI繁荣使计算能力紧张，Nvidia在1400万美元的资金回合中支持了印度的启动Simplismart，投注在帮助公司更高效地运行AI模型的软件上，在某些情况下，GPU更少。
Nvidia与Accel,Shastra VC，和Micromax一起参加了全主系列B轮比赛，由达拉斯风险资本主导投资。 这笔交易价值4年的启动时间约为1亿美元，其中包括新的资金。
目前公司正在大量花在GPU上，为AI应用提供动力，但以可靠和经济的规模运行这些模型仍然是一个挑战。 不同的工作量要求在速度、成本和业绩之间作出不同的权衡。 为满足业务要求而部署和操作人工智能模型往往也需要复杂的调整。 Simplismart 称的软件将大部分工作自动化，定制模型如何运行在现有计算基础设施上，而不是要求客户自己管理这些配置。
“实际上没有人需要最快或最便宜的推论”, Amritanshu Jain(上面的画面，左侧)，他补充说，公司需要以适合其具体应用的速度和成本运行AI模型。
Simplismart的软件介于AI模型和它们运行的计算基础设施之间，它可以优化一些因素，如一个模型如何快速响应，它处理多少个请求，以及它消耗多少计算能力，帮助公司从现有的GPU堆栈中获得更多。
基于孟加拉鲁鲁的启动为公司提供了通过自己的平台或在现有云和前提基础设施内部署和运行AI模型的方法。 它还为微调模型提供工具，随着需求变化自动对计算资源进行规模化，并监控性能。 此外，Simplismart允许云提供商使用其软件建立和运行自己对GPU基础设施的AI推论服务。
Simplismart 称帮助AI视频生成平台InVideo使用其软件将其GPU服务成本削减56%. 启动公司还与AI视频公司HeyGen和Higgsfield以及企业AI启动Ema等合作。 印度云供应商Yotta使用Simplismart的技术
Jain告诉TechCrunch，辛普利萨尔的年收入运行率在过去六个月里增长了14倍，这一数字是根据最近客户的使用情况而不是订约的经常性收入计算的，不包括转给基础设施供应商称公司的收入。 他没有披露确切的数字。
超过一半的辛普利萨尔的收入来自美国，尽管印度的启动拥有更多的客户，Jain 称。
Nvidia除了投资外，还与Simplismart合作技术集成，包括围绕其Dynamo推论软件，Jain告诉TechCrunch. 合作对象包括Simplismart的工程师和Nvidia的解决方案设计师，而Nvidia也帮助将启动引入潜在的客户和合作伙伴，Jain表示。
有趣的是，投资呈现出一个明显的悖论，因为AI芯片的主导供应商Nvidia正在支持软件，可以帮助公司以更少的GPU来承担一些工作量。 然而，Jain认为，提高AI推论的效率，最终会使其硬件对客户更具吸引力，并鼓励更广泛地采用AI应用，从而有利于Nvidia.
Simplismart正在日益拥挤的AI推论软件市场中为客户服务，与Nvidia本身的报价竞争，以及主要的云提供商和超标器，包括Amazon Web Services,Google Cloud，和Microsoft Azure. 越来越多的初创企业也正在开发工具，使AI模型运行得更快，成本更低，因为这一段的需求日益增加，而AI应用程序的需求不断增加，需要更好地利用昂贵的GPU资源。
“我们倾向于获胜的地方是人们必须对其服务级协议和TCO(所有权的总成本)更加严格的地方，”Jain 称提到要求高的业绩要求和严格的成本限制。 与为更快或更便宜的推论提供预设选项的对手不同，Simplismart旨在找到适合每个客户需要的平衡，他称。
Simplismart由前Oracle工程师Jain和前Google工程师Devansh Ghatak(上图，右图)创建于2022年，Simplismart由对子的经验建设和部署机器学习系统发展而来。 两人在BITS Pilani学习时相遇，创业者现在雇用了58人Jain 称。
Simplismart计划利用新资金扩大其在美国的存在，壮大团队，进一步投资开发其推论软件。 Jain告诉TechCrunch，美国仍然是启动的关键市场，因为它希望在那里扩大现有的客户基础。
供资公告紧接印度英语日报"经济时报"报道，辛普利斯马特正在会谈，以3亿至4亿美元的估值，筹集4000万至5000万美元。 被问及报告情况，贾因拒绝评论，表示启动并未积极筹款。 他补充说，B系列已经结束，不会延期。
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As the AI boom strains computing capacity, Nvidia has backed Indian startup Simplismart in a $14 million funding round, betting on software that helps companies run AI models more efficiently and, in some cases, with fewer GPUs.
Nvidia participated in the all-primary Series B round alongside Accel, Shastra VC, and Micromax, with Dallas Venture Capital leading the investment. The deal values the four-year-old startup at about $100 million, including the new funding.
Companies are nowadays spending heavily on GPUs to power AI applications, but running those models reliably and economically at scale remains a challenge. Different workloads demand distinct trade-offs between speed, cost, and performance. Deploying and operating AI models to meet business requirements often also requires complex adjustments. Simplismart says its software automates much of that work, tailoring how models run on existing computing infrastructure instead of requiring customers to manage those configurations themselves.
“Nobody actually needs the fastest or the cheapest inference,” Amritanshu Jain (pictured above, left), co-founder and CEO of Simplismart, said in an interview. He added that companies instead need to run AI models at speeds and costs that make sense for their specific applications.
Simplismart’s software sits between AI models and the computing infrastructure they run on and adjusts how models are deployed and managed to meet customers’ requirements. It says it can optimize factors such as how quickly a model responds, how many requests it handles, and how much computing power it consumes, helping companies get more out of 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. It also provides tools to fine-tune models, automatically scale computing resources as demand changes, and monitor performance. Additionally, Simplismart allows cloud providers build and operate their own AI inference services on GPU infrastructure using its software.
Simplismart says it helped AI video-generation platform InVideo cut its GPU serving costs by 56% using its software. The startup also works with AI video companies HeyGen and Higgsfield, as well as enterprise AI startup Ema, among others. Moreover, Indian cloud provider Yotta uses Simplismart’s 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, Jain told TechCrunch. The figure is based on recent customer usage rather than contracted recurring revenue and excludes revenue passed through to infrastructure providers, the company said. He did not disclose the exact figure.
More than half of Simplismart’s revenue comes from the U.S., despite the startup having more customers in India, Jain said.
Alongside its investment, Nvidia has been working with Simplismart on technical integrations, including around its Dynamo inference software, Jain told TechCrunch. The collaboration involves Simplismart’s engineers and Nvidia’s solutions architects, while Nvidia has also helped introduce the startup to potential customers and partners, Jain stated.
Interestingly, 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. Jain, however, 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.
Simplismart is serving 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 also developing tools to make AI models faster and cheaper to run as there is growing demand in this segment, driven by rising demand for AI applications and the need to make better use of expensive GPU resources.
“Where we tend to win is where people have to be more stringent on their SLAs [service-level agreements] and TCO [total cost of ownership],” Jain said, referring to demanding performance requirements and tight cost constraints. Unlike rivals that offer preset options for faster or cheaper inference, Simplismart aims to find a balance tailored to each customer’s needs, he said.
Founded in 2022 by former Oracle engineer Jain and former Google engineer Devansh Ghatak (pictured above, right), Simplismart grew out of the pair’s experience building and deploying machine-learning systems. The two met while studying at BITS Pilani, and the startup now employs 58 people, Jain said.
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. Jain told TechCrunch that the U.S. remains a key market for the startup as it looks to build on its existing customer base there.
The funding announcement follows a report by Indian English daily The Economic Times that Simplismart was in talks to raise $40 million to $50 million at a valuation of $300 million to $400 million. Asked about the report, Jain declined to comment, saying the startup was not actively fundraising. He added that the Series B was closed and would not be extended.
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