CEO and founder of Crowdgenai, Dr Sam Khoze, and COO, Susan Koehler
Is Crowdgenai a new competitor of Nvidia?
Crowdgenai redefines AI by proving that optimized CPU clusters can correspond to NVIDIA GPUs in the efficiency of AI formation while considerably reducing costs and energy consumption, while integrating blockchain -based watermark To ensure the possession of the data and the provenance in a world increasingly focused on ADI.
Artificial intelligence progresses at an unprecedented rate, but it faces two urgent challenges: data ownership and sustainability.
The rise of the deep childhood content – as the viral image generated by the Pope Francis in a white puffy jacket – exposes the ease with which AI can generate disinformation and manipulate reality.
False photos of Pape Francis carrying a large white pufferte coat, created with an AI image generator
Meanwhile, the carbon footprint of AI arises, the models powered by GPU consuming energy levels comparable to small countries. Companies now have difficulties with data protection, energy costs and AI transparency, highlighting the need for more sustainable and responsible AI ecosystem.
At Davos, I met an interesting business. Crowdgenai is an AI platform based on the processor which offers an alternative to the domination of the NVIDIA GPU and also has an offer to integrate the watermark based on blockchain for data traceability.
And large technological companies take note. Crowdgenai has partnerships with Google for startups and Microsoft Accelerator, while also collaborating the Environmental and Natural Resources Policy Program of the Stanford School and Wilson Sonini to stimulate innovation, sustainability and regulatory alignment in AI.
What is Crowdgenai? And how does he avoid NVIDIA GPUs?
Launched at the World Economic Forum 2025 in Davos, Crowdgenai presents an ecosystem fueled by IA-First, propelled by the CPU which makes the training IA more accessible, more profitable and responsible for the environment.
Unlike traditional AI pipelines that depend on high -energy GPUs, Crowdgenai operates largely available CPU clusters to distribute workloads, which makes training in AI possible on the existing infrastructure.
Beyond the efficiency of the calculation, the Crowdgenai traceid system guarantees that the content generated by AI is cryptographically watermark, allowing companies to prove ownership of their data and the results of the AI. This offers a track of provenance verified, reducing the risk of theft of intellectual property and disinformation of the AI.
Watermarking blockchain: prove the property of the data
A basic CrowdGenai innovation is Traceid, a blockchain -based watermark system that protects the content generated by AI. Each asset generated by AI – be it text, image or video – is invisibly anchored with an immutable cryptographic watermark, recorded on a large blockchain book.
An invisible filigree not detected by the human eye on the right and the original image on the left. … (+)
This guarantees authenticity, because the origin of the content and the modifications are traceable. It offers intellectual property protection by allowing companies to prove the ownership of the works generated by AI. Transparency is also improved, because the risks of disinformation are attenuated with verifiable AI content.
In the traditional AI paradigm, once you put your data for model training, visibility is lost. Crowdgenai returns this dynamic by ensuring that the contributors maintain the property. Thanks to its blockchain traceability, companies that contribute to data or models to a project have a immutable complaint On these assets. This opens the door to ethical data markets where companies can opt-in To share their data sets for AI training and be rewarded when these data sets are used. With Crowdgenai, the carefully organized data of a company can become an active income generating, sold or authorized to others in a controlled manner, rather than being scratched without authorization.
By combining processor efficiency with blockchain safety, Crowdgenai creates an AI model which is not only durable but also ethically governed and verifiable.
GPU passage to CPUs: Breaking Nvidia’s Hold
For more than a decade, NVIDIA GPU has been the OR for AI because of their ability to manage a massive parallel treatment. However, this GPU dependence has a high price: NVIDIA high -end fleas cost more than $ 30,000 each, and the formation of the AI model is incredibly gourmet, emitting hundreds of tons of CO2.
Crowdgenai questions this paradigm with a mathematical breakthrough – reinvent both mathematics and architecture behind the formation on AI models. By taking advantage of a new calculation model, Crowdgenai allows a network of processors to operate as a single GPU, distributing AI workloads on standard processors. This model optimizes the way in which the AI training tasks are structured, allowing processors to manage the multiplications of the complex matrix and the calculations of the tensor traditionally carried out by the GPUs.
Close-up shot of an NVIDIA GPU (photo of Smith Collection / Gado / Getty Images)
By moving the workloads of the AI on the infrastructure of the existing processor, Crowdgenai considerably reduces the barrier to the adoption of the AI. Companies and data centers can reduce the use of expensive and greedy GPU equipment while unlocking the full potential of underused CPU resources. This distributed AI training model reduces costs not only costs, but also reduces energy consumption, offering an evolutionary and more sustainable alternative to traditional AI focused on GPU.
A future AI based on the processor – not built on Nvidia
AI based on the processor reduces energy consumption up to 50%, which reduces emissions and cooling costs of the data center. Unlike the GPUs, CPUs have a longer lifespan and can be set up without costly material investments. In addition, the AI fueled by CPU makes AI training on a large scale accessible to more companies, removing the Nvidia monopoly.
Although optimized CPU clusters can set up AI models, they may not correspond to the gross speed of high -end GPUs. Many AI executives are optimized to the GPU, requiring adjustments to fully draw from the processor capacities. The AI industry has been based for GPUs for a long time, which means that early adoption can face the skepticism of companies.
Restabilization analysis for VS NVIDIA GPUS processors
For companies and data centers, Crowdgenai presents a convincing case. The king becomes a priority for companies that now experiment with AI.
By passing from workloads to AI to infrastructure based on the processor, companies can avoid the costs of high GPUs in the sky. Crowdgenai allows companies to use existing servers or affordable cloud processors to form models, reducing capital expenditure. Data centers can even monetize their inactive CPU capacity, transforming underused servers into income sources instead of letting them sit with low use. This more effective use of equipment leads to the cost of AI development project.
Sustainability is now a conference room priority and AI projects are faced with a meticulous examination for their carbon footprint. Did you know that the Microsoft data center used 700,000 liters of water during GPT-3 training? GPT-3 training has the same water cost as the production of 100 pounds of beef, almost double the quantity that an average American eats in one year.
Using Crowdgenai can help companies achieve ESG objectives by reducing energy consumption. Instead of building new GPU farms eager for power, companies take advantage of the effectiveness of distributed CPUs and avoid redundant infrastructure. This means lower electricity consumption and emissions by training employment. Companies can boast their AI initiatives as greener and more user -friendly, stimulating the reputation and compliance of companies.
A sustainable AI future with VS NVIDIA GPUS processors
Crowdgenai offers a new way to follow for AI: the one that is sustainable, profitable and ethically transparent. By proving that CPUs can fuel AI, it questions dependence on the GPU with great technology, which makes AI more widely accessible.
Meanwhile, its filigree and its traceability of the blockchain solve a major problem in AI: authenticity and property. As the adoption of AI accelerates, companies should consider the alternatives based on the processor not only for cost savings, but to ensure that their AI strategy aligns with sustainability and governance of Ethics.
Crowdgenai is not only an IA innovation – it is a movement to a responsible AI future and which may disrupt Nvidia.
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