As global startups continue to navigate the challenges of building AI products, Google is pushing the bar to give them everything they need in one place. Case in point: the company’s latest collaboration with Nvidia aimed at bringing the companies’ startup accelerators together.
Under the partnership, announced at the ongoing Google Cloud Next conference in Las Vegas, Google said it is combining its Google for Startups Cloud Program with Nvidia’s Inception initiative. The move will give startups access to everything they need – from cloud credits to technical resources and go-to-market support – to build AI applications and services and deliver value to their customers faster.
The development, which is expected to support hundreds of startups, comes as the extension of the partnership Google and Nvidia have had for years. Just recently, Google confirmed it will bring Nvidia’s new Grace Blackwell superchip to its cloud customers.
How will the joint accelerator effort help?
With the Google for Startups Cloud program, Google has been supporting startups at different stages of their journey with Google Cloud Platform resources, including credits, technical and business mentorship, and community networking opportunities. For AI-first startups in particular, the company has been providing up to $350,000 in cloud cost coverage, along with dedicated AI training and access to its AI ecosystem, including infrastructure, AI products like Vertex and foundation models.
Nvidia, with its Inception program, also supports AI startups at all stages of their lifecycle, although the focus is on helping them navigate the Nvidia ecosystem in this case. Eligible members get access to Nvidia’s software and hardware offerings, including the latest GPUs that handle language models, at preferred prices as well as free credits and discounts to access technical workshops and courses from its Deep Learning Institute. The members can even network with industry experts and investors to take their products to market.
Now, with both these programs coming together, members from one program will be eligible for benefits from the other. For instance, eligible members of Nvidia Inception will have an accelerated path to using Google Cloud infrastructure with access to Google Cloud credits up to $350,000.
Similarly, members enrolled in the Google initiative can join the one from Nvidia and gain access to its technological expertise, Deep Learning Institute course credits, hardware and software, among other resources. The participants will also get access to Inception Capital Connect, providing the opportunity to connect with venture capital firms looking to invest in the space.
This could be a major game-changer for AI startups, especially early-stage ones, that have been lagging due to the high costs associated with AI technologies and other related barriers.
In a blog post published today, Greg Estes from Nvidia also noted that emerging software makers from both programs will get fast-tracked onboarding to Google Marketplace, co-marketing and product acceleration support. This will help them reach customers and deliver value more quickly.
Estes did not share how many startups will be able to benefit from this exchange of offerings, but the work is expected to support hundreds of global startups. Nvidia Inception alone has 18,000 startups. However, Google’s exact number of participants remains unclear at this stage.
Nvidia's Inception program tracks AI startups.
Building on existing collaboration
The partnership between the two companies marks the latest move to accelerate the development of generative AI applications and services.
Before this, Nvidia worked with Google to make its latest Grace Blackwell AI platform available to all Google Cloud customers and launch optimizations across all Nvidia AI platforms for the Gemma family of open models, making them more performant and cost-efficient for customers.
Notably, Nvidia’s new NIM microservices also work with Google Kubernetes Engine (GKE) to give enterprises a streamlined approach to developing AI apps and deploying optimized models to production.