Nvidia
Indosat Ooredoo Hutchison, Nokia and Nvidia AI-RAN research center in Indonesia amongst telco skepticism
Indosat Ooredoo Hutchison (Indosat) Nokia, and Nvidia have officially launched the AI-RAN Research Centre in Surabaya, a strategic collaboration designed to advance AI-native wireless networks and edge AI applications across Indonesia. This collaboration, aims to support Indonesia’s digital transformation goals and its “Golden Indonesia Vision 2045.” The facility will allow researchers and engineers to experiment with combining Nokia’s RAN technologies with Nvidia’s accelerated computing platforms and Indosat’s 5G network.
According to the partners, the research facility will serve as a collaborative environment for engineers, researchers, and future digital leaders to experiment, learn, and co-create AI-powered solutions. Its work will centre on integrating Nokia’s advanced RAN technologies with Nvidia’s accelerated computing platforms and Indosat’s commercial 5G network. The three companies view the project as a foundation for AI-driven growth, with applications spanning education, agriculture, and healthcare.
The AI-RAN infrastructure enables high-performance software-defined RAN and AI workloads on a single platform, leveraging Nvidia’s Aerial RAN Computer 1 (ARC-1). The facility will also act as a distributed computing extension of Indosat’s sovereign AI Factory, a national AI platform powered by Nvidia, creating an “AI Grid” that connects datacentres and distributed 5G nodes to deliver intelligence closer to users.
Nezar Patria, vice minister of communication and digital affairs of the Republic of Indonesia said: “The inauguration of the AI-RAN Research Centre marks a concrete step in strengthening Indonesia’s digital sovereignty. The collaboration between the government, industry, and global partners such as Indosat, Nokia, and Nvidia demonstrates that Indonesia is not merely a user but also a creator of AI technology. This initiative supports the acceleration of the Indonesia Emas 2045 vision by building an inclusive, secure, and globally competitive AI ecosystem.”
Vikram Sinha, president director and CEO of Indosat Ooredoo Hutchison said: “As Indonesia accelerates its digital transformation, the AI-RAN Research Centre reflects Indosat’s larger purpose of empowering Indonesia. When connectivity meets compute, it creates intelligence, delivered at the edge, in a sovereign manner. This is how AI unlocks real impact, from personalised tutors for children in rural areas to precision farming powered by drones. Together with Nokia and Nvidia, we’re building the foundation for AI-driven growth that strengthens Indonesia’s digital future.”
From a network perspective, the project demonstrates how AI-RAN architectures can optimize wireless network performance, energy efficiency, and scalability through machine learning–based radio signal processing.
Ronnie Vasishta, senior vice president of telecom at Nvidia added: “The AI Grid is the biggest opportunity for telecom providers to make AI as ubiquitous as connectivity and distribute intelligence at scale by tapping into their nationwide wireless networks.”
Pallavi Mahajan, chief technology and AI officer at Nokia said: “This initiative represents a major milestone in our journey toward the future of AI-native networks by bringing AI-powered intelligence into the hands of every Indonesian.”
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Wireless Telcos are Skeptical about AI-RAN:
According to Light Reading, the AI RAN uptake is facing resistance from telcos. The problem is Nvidia’s AI GPUs are very costly and not GPUs power-efficient enough to reside in wireless base stations, central offices or even small telco data centers.
Nvidia references 300 watts for the power consumption of ARC-Pro, which is much higher than the peak of 40 watts that Qualcomm claimed more than two years ago for its own RAN silicon when supporting throughput of 24 Gbit/s. How ARC-Pro would measure up on a like-for-like basis in a commercial network is obviously unclear.
Nvidia also claims a Gbit/s-per-watt performance “on par with” today’s traditional custom silicon. Yet the huge energy consumption of GPU-filled telco data centers does not bear that out.
“Is there a case for a wide-area indiscriminate rollout? I am not sure,” said Verizon CTO Yago Tenorio, during the Brooklyn 6G Summit, another telecom event, last week. “It depends on the unit cost of the GPU, on the power efficiency of the GPU, and the main factor will always be just doing what’s best for the basestation. Don’t try to just overcomplicate the whole thing monetizing that platform, as there are easier ways to do it.”
“We have no way to justify a business case like that,” said Bernard Bureau, the vice president of wireless strategy for Canada’s Telus, at FYUZ. “Our COs [central offices] are not necessarily the best places to run a data center. It would mean huge investments in space and power upgrades for those locations, and we’ve got sunk investment that can be leveraged in our cell sites.”
Light Reading’s Iain Morris wrote, “Besides turning COs into data centers, operators would need to invest in fiber connections between those facilities and their masts.”
How much spectral efficiency can be gained by using Nvidia GPUs as RAN silicon?
“It’s debatable if it’s going to improve the spectral efficiency by 50% or even 100%. It depends on the case,” said Tenorio. Whatever the exact improvement, it would be “really good” and is something the industry needs, he told the audience.
In April, Nokia’s rival Ericsson said it had tested “AI-native” link adaptation, a RAN algorithm, in the network of Bell Canada without needing any GPU. “That’s an algorithm we have optimized for decades,” said Per Narvinger, the head of Ericsson’s mobile networks business group. “Despite that, through a large language model, but a really small one, we gained 10% of spectral efficiency.”
Before Nvidia invested in Nokia, the latter claimed to have sufficient AI and machine-learning capabilities in the custom silicon provided by Marvell Technology, its historical supplier of 5G base station chips.
Executives at Cohere Technology praises Nvidia’s investment in Nokia, seeing it as an important AI spur for telecom. Yet their own software does not run on Nvidia GPUs. It promises to boost spectral efficiency on today’s 5G networks, massively reducing what telcos would have to spend on new radios. It has won plaudits from Vodafone’s Pignatelli as well as Bell Canada and Telstra, both of which have invested in Cohere. The challenge is getting the kit vendors to accommodate a technology that could hurt their own sales. Regardless, Bell Canada’s recent field trials of Cohere have used a standard Dell server without GPUs.
Finally, if GPUs are so critical in AI for RAN, why has neither Ericsson or Samsung using Nvidia GPU’s in their RAN equipment?
Morris sums up:
“Currently, the AI-RAN strategy adopted by Nokia looks like a massive gamble on the future. “The world is developing on Nvidia,” Vasishta told Light Reading in the summer, before the company’s share price had gained another 35%. That vast and expanding ecosystem holds attractions for RAN developers bothered by the diminishing returns on investment in custom silicon.”
“Intel’s general-purpose chips and virtual RAN approach drew interest several years ago for all the same reasons. But Intel’s recent decline has made Nvidia shine even more brightly. Telcos might not have to worry. Nvidia is already paying a big 5G vendor (Nokia) to use its technology. For a company that is so outrageously wealthy, paying a big operator to deploy it would be the next logical step.
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References:
https://capacityglobal.com/news/indosat-nokia-and-nvidia-launch-ai-ran-research-centre-in-indonesia/
https://www.telecoms.com/ai/indosat-nokia-and-nvidia-open-ai-ran-research-centre-in-indonesia
https://www.lightreading.com/5g/nokia-and-nvidia-s-ai-ran-plan-hits-telco-resistance
https://resources.nvidia.com/en-us-aerial-ran-computer-pro
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Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry
Amdocs and NVIDIA today announced they are collaborating to optimize large language models (LLMs) to speed adoption of generative AI applications and services across the $1.7 trillion telecommunications and media industries.(1)
Amdocs and NVIDIA will customize enterprise-grade LLMs running on NVIDIA accelerated computing as part of the Amdocs amAIz framework. The collaboration will empower communications service providers to efficiently deploy generative AI use cases across their businesses, from customer experiences to network provisioning.
Amdocs will use NVIDIA DGX Cloud AI supercomputing and NVIDIA AI Enterprise software to support flexible adoption strategies and help ensure service providers can simply and safely use generative AI applications.
Aligned with the Amdocs strategy of advancing generative AI use cases across the industry, the collaboration with NVIDIA builds on the previously announced Amdocs-Microsoft partnership. Service providers and media companies can adopt these applications in secure and trusted environments, including on premises and in the cloud.
With these new capabilities — including the NVIDIA NeMo framework for custom LLM development and guardrail features — service providers can benefit from enhanced performance, optimized resource utilization and flexible scalability to support emerging and future needs.
“NVIDIA and Amdocs are partnering to bring a unique platform and unmatched value proposition to customers,” said Shuky Sheffer, Amdocs Management Limited president and CEO. “By combining NVIDIA’s cutting-edge AI infrastructure, software and ecosystem and Amdocs’ industry-first amAlz AI framework, we believe that we have an unmatched offering that is both future-ready and value-additive for our customers.”
“Across a broad range of industries, enterprises are looking for the fastest, safest path to apply generative AI to boost productivity,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration with Amdocs will help telco service providers automate personalized assistants, service ticket routing and other use cases for their billions of customers, and help the telcos analyze and optimize their operations.”
Amdocs counts more than 350 of the world’s leading telecom and media companies as customers, including 27 of the world’s top 30 service providers.(2) With more than 1.7 billion daily digital journeys, Amdocs platforms impact more than 3 billion people around the world.
NVIDIA and Amdocs are exploring a number of generative AI use cases to simplify and improve operations by providing secure, cost-effective and high-performance generative AI capabilities.
Initial use cases span customer care, including accelerating customer inquiry resolution by drawing information from across company data. On the network operations side, the companies are exploring how to proactively generate solutions that aid configuration, coverage or performance issues as they arise.
(1) Source: IDC, OMDIA, Factset analyses of Telecom 2022-2023 revenue.
(2) Source: OMDIA 2022 revenue estimates, excludes China.
Editor’s Note:
- Language models: These models, like OpenAI’s GPT-3, generate human-like text. One of the most popular examples of language-based generative models are called large language models (LLMs).
- Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences.
- Generative adversarial networks (GANs): These models use two neural networks, a generator, and a discriminator.
- Unimodal models: These models only accept one data input format.
- Multimodal models: These models accept multiple types of inputs and prompts. For example, GPT-4 can accept both text and images as inputs.
- Variational autoencoders (VAEs): These deep learning architectures are frequently used to build generative AI models.
- Foundation models: These models generate output from one or more inputs (prompts) in the form of human language instructions.
https://www.nvidia.com/en-us/glossary/data-science/generative-ai/
https://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/
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Nvidia Survey Reveals How Telcos Plan to Use AI; Quantifying ROI is a Challenge
A Nvidia sponsored survey of more than 400 telecommunications industry professionals from around the world found a cautious tone in how they plan to define and execute on their AI strategies. Virtually every telco is already engaged with AI in some way, although mostly at an early stage. NVIDIA’s first “State of AI in Telecommunications” survey consisted of questions covering a range of AI topics, infrastructure spending, top use cases, biggest challenges and deployment models. The survey was conducted over eight weeks between mid-November 2022 and mid-January 2023.
Amid skepticism about the money-making potential of 5G, telecoms see efficiencies driven by AI as the most likely path for returns on investment. 93% of those responding to questions about undertaking AI projects at their own companies appear to be substantially underinvesting in AI as a percentage of annual capital spending.
Some 50% of respondents reported spending less than $1 million last year on AI projects; a year earlier, 60% of respondents said they spent less than $1 million on AI. Just 3% of respondents spent over $50 million on AI in 2022.
The reasons cited for such cautious spending? Some 44% of respondents reported an inability to adequately quantify return on investment, which illustrates a mismatch between aspirations and the reality in introducing AI-driven solutions. 34% cited an insufficient number of data scientists as the second-biggest challenge.
The biggest telco objectives for AI are to: optimize operations (60%), lower costs (44%) and enhance customer engagement (35%). Respondents cited use cases ranging from cell site planning and truck-route optimization to recommendation engines.
Just over a third of respondents said they had been using AI for more than six months. 31% said they’re still weighing different options, 18% reported being still in a trial phase and only 5% said they had no AI plans at all. Most industry execs say they see AI technologies will positively impact their business – 65% agreed AI was important to their company’s success, and 59% said it would become a source of competitive advantage.
Operators are spending a fraction of their capex budgets on AI projects – last year half said they spent less than $1 million on AI. At the top end, 2% spent more than $50 million in 2021, with that number rising to 3% in 2022.
The latest AI Index compiled by Stanford University puts telcos at the forefront of AI deployment. Using its own data and that from a McKinsey study, it found that the highest level of AI adoption is in product or service development by hi-tech companies and telcos (45%), followed by AI in service operations (45%).
The biggest single application in any industry was natural language text understanding deployed by 34% of hi-tech and telco firms, with 28% implementing AI-based computer vision and 25% using virtual agents.
- Moving from proof of concept to production/scale 47%
- Economic uncertainty 46%
- Infrasctructure upgrades 46%
- Market differentiation 34%
- Change in priority of data science 20%
- 92% will either increase or maintain their AI spend in 2023.
References:
https://blogs.nvidia.com/blog/2023/02/21/telco-survey-ai/
https://www.nvidia.com/en-us/lp/industries/telecommunications/state-of-ai-in-telecom-survey-report/
https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf
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