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Ola Krutrim, Infosys, and Bharti Airtel to Leverage Intel Gaudi 2 to Unleash AI Innovations
Ola Krutrim, Infosys, and Bharti Airtel to Leverage Intel Gaudi 2 to Unleash AI Innovations
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Ola Krutrim, Infosys, and Bharti Airtel to Leverage Intel Gaudi 2 to Unleash AI Innovations

Krutrim is currently pre-training a larger foundational model on an Intel Gaudi 2 cluster.

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Key takeaways

  • Leverage Intel Gaudi 2 to enhance AI capabilities across enterprise sectors in India.
  • Bharti Airtel utilizes telecom data to improve customer experiences and generate new revenue streams.
  • Infosys partners with Intel to integrate advanced AI technologies into its offerings for business value.
  • Ola Krutrim pre-trains foundational models on Intel Gaudi 2, achieving superior price/performance ratios.
  • Intel expands collaborations with several tech firms to drive AI innovations and solutions.
5 min

In a bid to empower enterprise customers in India with innovative AI solutions, Intel has embarked on several collaborations and deployments which were announced at Intel Vision 2024.

Bharti Airtel aims to harness its extensive telecom data to enhance AI capabilities, thereby enriching customer experiences and exploring new revenue avenues in the digital realm.

Infosys has announced a strategic partnership with Intel, integrating Intel technologies such as 4th and 5th Gen Intel Xeon processors, Intel Gaudi 2 AI accelerators, and Intel Core Ultra into Infosys Topaz. This collaboration aims to offer AI-first services, solutions, and platforms to accelerate business value through generative AI technologies.

Ola Krutrim is utilising Intel Gaudi 2 clusters to pre-train and fine-tune its foundational models with generative capabilities in ten languages, achieving industry-leading price/performance ratios compared to existing market solutions. Additionally, Krutrim is currently pre-training a larger foundational model on an Intel Gaudi 2 cluster, further advancing its AI capabilities.

Along with this, Intel has also announced its partnerships with Bosch, CtrlS, IBM, IFF, Landing AI, NAVER, NielsenIQ, Roboflow and Seekr.

Recently, ManageEngine, the IT enterprise wing of Zoho, confirmed that it would be investing $10 million in NVIDIA, AMD and Intel, aiming to unleash generative AI offerings for its customers. 

Santhosh Vishwanathan, the vice president and MD at Intel, posted that the company has collaborated with Zoho to leverage Intel® Xeon® processors and the OpenVINO™ toolkit to empower its Video AI Assistant.

Following this development, Zoho is now collaborating with Intel to optimise and accelerate its video AI workloads for users. This will empower efficiency, reduce total cost of ownership (TCO), and optimise performance.

At the Intel Vision 2024, Intel has announced the release of its latest AI accelerator, the Intel Gaudi 3, set to revolutionise AI systems. The Gaudi 3 boasts the capability to power AI systems with tens of thousands of accelerators interconnected via Ethernet, setting a new standard in AI processing.

The Intel Gaudi 3 accelerator is slated for availability to OEMs, including Dell Technologies, HPE, Lenovo, and Supermicro, in the second quarter of 2024.

Intel has also forged partnerships with Google Cloud, Thales, and Cohesity to utilise Intel's confidential computing capabilities within their cloud environments. This encompasses Intel Trust Domain Extensions (Intel TDX), Intel Software Guard Extensions (Intel® SGX), and Intel's attestation service. 

Users have the opportunity to execute their AI models and algorithms within a secure execution environment known as a Trusted Execution Environment (TEE), while also benefiting from Intel's trust services for autonomously validating the integrity of these TEEs.

Compared to the NVIDIA H100, the Intel Gaudi 3 is projected to accelerate time-to-train by an average of 50% across various models, including Llama 2 models with 7B and 13B parameters, as well as the GPT-3 175B parameter model. 

Furthermore, the Gaudi 3 accelerator is expected to outperform the H100 by 50% in inference throughput on average and achieve a 40% increase in inference power-efficiency across different parameter models.

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