JAKARTA, Indonesia, April 29, 2026 /PRNewswire/ — PT Vale Indonesia Tbk (“PT Vale” or the “Company”) has secured a US$750 million Sustainability-Linked Loan (SLL) facility, including a US$250 million greenshoe option, marking its debut in the syndicated loan market and reinforcing its sustainable finance strategy. Supported by 14 international banks and 1.7 times oversubscribed, the facility reflects strong lender confidence in PT Vale’s credit profile, strategic project pipeline, and ESG-linked growth trajectory.

Structured under PT Vale’s Sustainability-Linked Financing Framework, the facility is linked to two performance metrics: reducing carbon emissions intensity and increasing renewable energy consumption. Both KPIs received a “strong” rating from an independent Second Party Opinion provider, aligned with the Paris Agreement’s 1.5°C pathway and Indonesia’s Nationally Determined Contributions.

As demand for responsibly produced nickel grows, driven by electrification, energy storage, and global decarbonisation, PT Vale is positioned as a relatively low-carbon producer supported by hydropower-based operations.

President Director and Chief Executive Officer of PT Vale, Bernardus Irmanto, stated: “This facility marks an important step in our journey to align our financing strategy with our decarbonisation agenda and long-term growth ambitions. We remain committed to delivering high-quality nickel with a lower carbon footprint, while supporting Indonesia’s downstreaming agenda and contributing meaningfully to the global energy transition.”

Harapman Kasan, Wholesale Banking Director, UOB Indonesia, stated: “As Southeast Asia’s nickel sector continues to evolve, the role of well-structured transition financing becomes increasingly critical. This transaction reflects our commitment to aligning financing structures with measurable sustainability objectives, while supporting Indonesia’s broader industrial and energy transition priorities.”

Mike Zhang, Global Head of Metals & Mining, Institutional Banking at DBS Bank, added that the metals and mining sector plays a pivotal role in enabling the energy transition and must demonstrate credible, measurable progress in sustainability.

Ken Matsuo, President Director of PT Bank Mizuho Indonesia, commented: “The energy sector is a cornerstone of Indonesia’s economy, and we are pleased to support PT Vale’s inaugural syndicated loan. Despite market volatility, the strong participation and oversubscription underscore confidence in PT Vale’s business model. We see ESG integration in financing structures such as this as a critical enabler of a sustainable energy transition.”

PT Vale will also allocate financial benefits from sustainability-linked margin adjustments to community development programmes, extending ESG impact beyond operations.

Media Contact:
Vanda Kusumaningrum 
Head of Corporate Communications
PT Vale Indonesia Tbk.
Vanda.Kusumaningrum@vale.com

BEIJING, April 29, 2026 /PRNewswire/ — At the 19th Beijing International Automotive Exhibition (hereinafter referred to as “Auto China”), DeepRoute.ai held a press conference to showcase its latest advances in Physical AI. During the event, CEO Maxwell Zhou reflected on the company’s founding mission and outlined its latest advances and vision in Physical AI. Chief Scientist Chong Ruan then delivered his first public keynote, providing a systematic overview of the company’s technical architecture around its Foundation Model. The event marks a milestone in DeepRoute.ai’s push to establish leadership in Physical AI and shape the direction of next-generation advanced intelligent driving systems.

Maxwell Zhou: Aiming to Become the AI Infrastructure of the Physical World

Opening the press conference, CEO Maxwell Zhou recounted a traffic accident that occurred near him in the early days of his startup journey in 2016. “At that time, I wondered whether we could use AI technology to save more lives,” Zhou said. He acknowledged that current advanced intelligent driving systems are not yet perfect, with MPCI (Miles Per Critical Intervention) in urban areas still measured in the tens of kilometers, but noted that available data indicates their safety is already several times higher than that of human drivers. “We believe that within the next two to three years, as large models continue to develop their comprehension capabilities, we will achieve truly safe advanced intelligent driving systems.”

Zhou set out a long-term vision for DeepRoute.ai: “I hope that in the future, the company will become the AI infrastructure of the physical world, serving as a foundational capability that sustains real-world operations, much like telecommunications and electricity. When people talk about intelligence in the physical world, DeepRoute.ai should be an essential part of that foundation.”

Chief Scientist Chong Ruan’s Keynote: Updates on the Foundation Model

Chong Ruan, former Head of R&D at DeepSeek and a core researcher in multimodal AI, made his public debut as DeepRoute.ai’s Chief Scientist at this event. He provided a systematic overview of the Foundation Model and the latest progress in building cognitive capabilities for the advanced intelligent driving system.

Ruan noted that as intelligent driving enters the mass production phase, earlier approaches relying on smaller models have shown limited progress in system stability and consistent user adoption. These systems still exhibit performance fluctuations in complex, edge-case scenarios, and a reliable foundation of trust in the driving experience has yet to be established. To address this, DeepRoute.ai has developed a next-generation technical approach centred on the Foundation Model.

The Foundation Model unifies driving decision-making, scene understanding, and behaviour evaluation within a single architecture. By leveraging greater model scale, higher data quality, and a faster data-driven closed-loop, it enables the continuous improvement of the advanced intelligent driving system. Under this framework, the iteration cycle of the data-driven closed-loop has been cut from approximately five days to around 12 hours, significantly improving operational efficiency.

Ruan also noted that the value of the Foundation Model extends beyond product capabilities and is now influencing how the organisation operates. “From internal knowledge base Q&A and automated code generation to cross-departmental collaboration and autonomous experimental analysis, AI is reshaping our R&D and management workflows.”

Cross-Industry Dialogue: Focusing on the Core Proposition of “AI for what”

At the press conference, DeepRoute.ai also hosted an “AI Talk” industry dialogue themed “AI for what.” The panel was moderated by Li Zhang, Professor at the School of Data Science at Fudan University. Participants included Jian Huo, General Manager of Automotive and Energy Solutions at Alibaba Cloud; Yinghao Xu, Assistant Professor at HKUST CSE and Staff Research Scientist at RobbyAnt; Hao Jingfang, Hugo Award-winning author, Founder of Tong Xing College, and holder of a PhD in Economics and an M.S. in Astrophysics from Tsinghua University; and Chong Ruan.

Unlike traditional product presentations, the dialogue was structured around a series of probing questions: from the capability boundaries of large models in real-world environments and the debate between World Models and VLA models, to the broader societal impact of Physical AI. Each question built on the last, keeping the discussion focused on the fundamental question of what AI is ultimately for.

Propelled by the Data Flywheel for Scaled Evolution, Fully Entering the Era of Physical AI

During the event, DeepRoute.ai also previewed its Cabin-Driving Integration Agent. Rather than functioning as a conventional voice assistant or in-vehicle infotainment system, the feature is designed to evolve the system into an “AI Brain” capable of understanding user needs and responding proactively to complex scenarios.

DeepRoute.ai reports that mass production vehicles equipped with its Urban NOA solution have now exceeded 300,000 units. Over the past year, vehicles running DeepRoute.ai’s active safety systems have accumulated over 1.3 billion kilometres of real-world road operation and 44.8 million hours of user driving time. This volume of real-world data, generated through the Data Flywheel, both validates the system’s safety performance and provides a critical foundation for the ongoing optimisation of the Foundation Model.

By 2026, DeepRoute.ai plans to grow mass production delivery of its advanced intelligent driving system past one million units. The company also aims to increase its MPCI metric to over 1,000 kilometres and raise its active daily use rate to over 50%. These targets are intended to drive continued improvements in system safety, stability, and user experience, advancing the commercial deployment of Physical AI at scale.

DUBLIN, April 29, 2026 /PRNewswire/ — Temu, the global e-commerce platform, and QIMA, a leading testing, inspection, and certification company, today announced a partnership to strengthen product compliance and safety across the Temu platform. Under the partnership, QIMA’s testing and certification services will be integrated directly into Temu’s Seller Center, making compliance resources accessible to sellers.

QIMA will deliver independent product testing, on-site factory inspections, seller training programs, and digital compliance tools. Product testing will cover four initial categories: electrical and electronic goods, jewelry and gemstones, food contact materials, and light industrial products. Testing will be conducted against applicable regulatory and safety standards. QIMA will also conduct on-site factory inspections for selected sellers to verify production processes and supply chain practices at the source.

The Temu-QIMA partnership also includes structured training programs designed to help sellers better understand testing standards and regulatory requirements across markets, along with regular roundtables and workshops on evolving rules, policy developments, and compliance approaches.

“As e-commerce platforms serve more markets and more product categories, independent compliance infrastructure becomes essential, not optional,” said Pierre-Nicolas Disser, CEO of Consumer Products, QIMA. “This partnership, and particularly the integration into Temu’s Seller Center, is a step toward making compliance testing and certification a routine part of how sellers operate. That’s exactly the kind of work QIMA’s global network was built for.”

The partnership with QIMA builds on Temu’s broader product safety and compliance program. In 2025, the company invested approximately US$100 million globally in compliance, product safety, and quality control, with plans to double that investment in 2026. To date, Temu has established cooperation with more than 60 independent testing institutions worldwide. The partnership with QIMA represents one of the first integration of third-party compliance tools directly into the Temu Seller Center workflow.

“Temu prioritizes the safety of products on our platform, and our partnership with QIMA is a concrete step in that direction,” said a Temu spokesperson. “Together with QIMA, we are focused on providing consumers with a safe and trustworthy shopping experience, while making compliance resources more accessible to sellers on our platform.”

About Temu

Temu is a global e-commerce platform connecting consumers with millions of manufacturers, brands, and business partners. Operating in more than 90 markets worldwide, Temu is committed to providing affordable, high-quality products that enable customers to live better lives.

About QIMA

At QIMA, we are on a mission to help our clients make products consumers can trust. We have developed compliance solutions for testing, inspection and certification (TIC) that enable supply chain agility, sustainability, and product innovation. Our services are used by 30,000 businesses globally in the consumer products, agri-food and life sciences industries. What truly sets us apart is our unique culture of relentless care for our clients, and a commitment to offering intuitive solutions that blend deep tech and human intelligence; this is how QIMA continues to disrupt the Testing, Inspection and Certification industry.

CAMBRIDGE, England, April 29, 2026 /PRNewswire/ — myrtle.ai, a recognized leader in accelerating machine learning inference, today announced that a stack featuring its VOLLO® product has recently been audited by STAC®, a leading benchmark authority for the finance industry.^[1] The results, unveiled at the STAC Summit in London today, clearly demonstrate the latency benefits of an FPGA-based solution for ML inference in financial trading and related applications.

Myrtle.ai Halves Latency in Financial Machine Learning Inference Benchmark Record with VOLLO

STAC-ML (Markets) Inference is the technology benchmark standard for solutions that may be used to run inference on real-time market data. Designed by quants and technologists from some of the world’s leading financial firms, STAC-ML Markets (Inference) reports the performance, resource efficiency, and quality of any technology stack capable of performing inference using the provided models.

VOLLO achieved latencies as low as 2 microseconds (99^th percentile) while also exhibiting excellent results in throughput and efficiency. Across all three benchmark models, VOLLO inferred in lower latency (99^th percentile) than all previously audited systems, halving its previous record. Such low, deterministic latency enables users to make more intelligent decisions using more complex models faster than in the past, giving them a competitive advantage in trading, risk analysis, quotes and many other trading-related activities.

With hundreds of thousands of hours of production trading under its belt, VOLLO is generating alpha for many of the world’s leading trading firms today. Those firms have developed and trained a wide range of models in standard ML tool flows before compiling them into VOLLO and then running them on their choice of FPGA-based hardware platform.

In the system under test, VOLLO ran on the standard form factor FBAP4@VP18-2L0S PCIe accelerator card from Silicom, containing an AMD Versal™ Premium series VP1802 Adaptive SoC and installed in a Supermicro AS -2015CS-TNR server. The AMD Versal Premium Series Adaptive SoC provides PCIe Gen5x8 and more than 3.3M programmable LUTs, making it well suited to low latency inference applications.

“Since VOLLO first exploited the full potential of FPGAs in this STAC benchmark in 2023, we have worked with our customers to further reduce latencies, expand the variety and size of models that VOLLO can run, and grow the range of platforms it can run on,” said Peter Baldwin, CEO of myrtle.ai.  “We’re excited to work with AMD, Silicom and Supermicro on this benchmark, to demonstrate how our combined technologies can enable ultra-low latency AI inference in quant trading.”

“The future of financial markets will be shaped by AI systems that can interpret data and act on it in near real time,” said Girish Malipeddi, director for Data Center FPGA business, AMD. “With AMD Versal™ Premium series adaptive SoCs at the foundation, myrtle.ai’s VOLLO demonstrates how advanced, low-latency inference can help unlock a new generation of intelligent trading infrastructure.“

“Supermicro continues to address a wide range of markets with our AMD systems, which were used for this STAC-ML benchmark,” said Michael McNerney, Senior Vice President Marketing and Network Security, Supermicro. “Our servers address the most challenging workloads in the financial services industry, and together with partners, we are able to deliver top-end performance with very low latencies for machine learning workloads.”

Anders Poulsen, VP Solutions at Silicom Denmark, said: “We’re pleased that myrtle.ai selected Silicom’s Artena accelerator card, based on AMD Versal Premium, for these tests. Built around one of the largest FPGAs in a PCIe form factor, Artena is an ideal platform for VOLLO. Together, VOLLO and our low-latency hardware deliver deterministic, microsecond-level inference for demanding trading workloads.”

ML developers can evaluate today how their models could perform on VOLLO, without the need for any FPGA tools or expertise. For more details go to vollo.myrtle.ai or contact myrtle.ai today at fintech@myrtle.ai.

The full benchmark results are available in the STAC Report (SUT ID MRTL260323) at http://www.STACresearch.com/MRTL260323.

About myrtle.ai

Myrtle.ai is an AI/ML software company that delivers world-class inference accelerators on FPGA-based platforms from all the leading FPGA suppliers. With broad neural network expertise, myrtle.ai has delivered accelerators for applications including fintech, wireless telecoms, LLMs, speech processing, and recommendation.

VOLLO, VOLLO Accelerator and the VOLLO logo are registered trademarks of myrtle.ai.

“STAC” and all STAC names are trademarks or registered trademarks of the Strategic Technology Analysis Center, LLC. AMD, the AMD logo, Versal, and combinations thereof are trademarks of Advanced Micro Devices, Inc. 

^[1] www.STACresearch.com/MRTL260323

CAMBRIDGE, England, April 29, 2026 /PRNewswire/ — myrtle.ai, a recognized leader in accelerating machine learning inference, today announced that a stack featuring its VOLLO® product has recently been audited by STAC®, a leading benchmark authority for the finance industry.^[1] The results, unveiled at the STAC Summit in London today, clearly demonstrate the latency benefits of an FPGA-based solution for ML inference in financial trading and related applications.

Myrtle.ai Halves Latency in Financial Machine Learning Inference Benchmark Record with VOLLO

STAC-ML (Markets) Inference is the technology benchmark standard for solutions that may be used to run inference on real-time market data. Designed by quants and technologists from some of the world’s leading financial firms, STAC-ML Markets (Inference) reports the performance, resource efficiency, and quality of any technology stack capable of performing inference using the provided models.

VOLLO achieved latencies as low as 2 microseconds (99^th percentile) while also exhibiting excellent results in throughput and efficiency. Across all three benchmark models, VOLLO inferred in lower latency (99^th percentile) than all previously audited systems, halving its previous record. Such low, deterministic latency enables users to make more intelligent decisions using more complex models faster than in the past, giving them a competitive advantage in trading, risk analysis, quotes and many other trading-related activities.

With hundreds of thousands of hours of production trading under its belt, VOLLO is generating alpha for many of the world’s leading trading firms today. Those firms have developed and trained a wide range of models in standard ML tool flows before compiling them into VOLLO and then running them on their choice of FPGA-based hardware platform.

In the system under test, VOLLO ran on the standard form factor FBAP4@VP18-2L0S PCIe accelerator card from Silicom, containing an AMD Versal™ Premium series VP1802 Adaptive SoC and installed in a Supermicro AS -2015CS-TNR server. The AMD Versal Premium Series Adaptive SoC provides PCIe Gen5x8 and more than 3.3M programmable LUTs, making it well suited to low latency inference applications.

“Since VOLLO first exploited the full potential of FPGAs in this STAC benchmark in 2023, we have worked with our customers to further reduce latencies, expand the variety and size of models that VOLLO can run, and grow the range of platforms it can run on,” said Peter Baldwin, CEO of myrtle.ai.  “We’re excited to work with AMD, Silicom and Supermicro on this benchmark, to demonstrate how our combined technologies can enable ultra-low latency AI inference in quant trading.”

“The future of financial markets will be shaped by AI systems that can interpret data and act on it in near real time,” said Girish Malipeddi, director for Data Center FPGA business, AMD. “With AMD Versal™ Premium series adaptive SoCs at the foundation, myrtle.ai’s VOLLO demonstrates how advanced, low-latency inference can help unlock a new generation of intelligent trading infrastructure.“

“Supermicro continues to address a wide range of markets with our AMD systems, which were used for this STAC-ML benchmark,” said Michael McNerney, Senior Vice President Marketing and Network Security, Supermicro. “Our servers address the most challenging workloads in the financial services industry, and together with partners, we are able to deliver top-end performance with very low latencies for machine learning workloads.”

Anders Poulsen, VP Solutions at Silicom Denmark, said: “We’re pleased that myrtle.ai selected Silicom’s Artena accelerator card, based on AMD Versal Premium, for these tests. Built around one of the largest FPGAs in a PCIe form factor, Artena is an ideal platform for VOLLO. Together, VOLLO and our low-latency hardware deliver deterministic, microsecond-level inference for demanding trading workloads.”

ML developers can evaluate today how their models could perform on VOLLO, without the need for any FPGA tools or expertise. For more details go to vollo.myrtle.ai or contact myrtle.ai today at fintech@myrtle.ai.

The full benchmark results are available in the STAC Report (SUT ID MRTL260323) at http://www.STACresearch.com/MRTL260323.

About myrtle.ai

Myrtle.ai is an AI/ML software company that delivers world-class inference accelerators on FPGA-based platforms from all the leading FPGA suppliers. With broad neural network expertise, myrtle.ai has delivered accelerators for applications including fintech, wireless telecoms, LLMs, speech processing, and recommendation.

VOLLO, VOLLO Accelerator and the VOLLO logo are registered trademarks of myrtle.ai.

“STAC” and all STAC names are trademarks or registered trademarks of the Strategic Technology Analysis Center, LLC. AMD, the AMD logo, Versal, and combinations thereof are trademarks of Advanced Micro Devices, Inc. 

^[1] www.STACresearch.com/MRTL260323

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