The announcements, made during its Spring Launch 2025 online event, underscore the drive by Alibaba to accelerate AI innovation and adoption on a global scale.

The digital technology and intelligence arm of Alibaba is focusing on meeting increasing demand for AI-driven digital transformation worldwide.

Selina Yuan, President of International Business at Alibaba Cloud Intelligence, said: “We are launching a series of Platform-as-a-Service（PaaS) and AI capability updates to meet the growing demand for digital transformation from across the globe.

“These upgrades allow us to deliver even more secure and high-performance services that empower businesses to scale and innovate in an AI-driven world.”

Alibaba expands access to foundational AI models

Central to the announcement is the broadened availability of Alibaba Cloud’s proprietary Qwen large language model (LLM) series for international clients, initially accessible via its Singapore availability zones.

This includes several specialised models:

• Qwen-Max: A large-scale Mixture of Experts (MoE) model.
• QwQ-Plus: An advanced reasoning model designed for complex analytical tasks, sophisticated question answering, and expert-level mathematical problem-solving.
• QVQ-Max: A visual reasoning model capable of handling complex multimodal problems, supporting visual input and chain-of-thought output for enhanced accuracy.
• Qwen2.5-Omni-7b: An end-to-end multimodal model.

These additions provide international businesses with more powerful and diverse tools for developing sophisticated AI applications.

Platform enhancements power AI scale

To support these advanced models, Alibaba Cloud’s Platform for AI (PAI) received significant upgrades aimed at delivering scalable, cost-effective, and user-friendly generative AI solutions.

Key enhancements include the introduction of distributed inference capabilities within the PAI-Elastic Algorithm Service (EAS). Utilising a multi-node architecture, this addresses the computational demands of super-large models – particularly those employing MoE structures or requiring ultra-long-text processing – to overcome limitations inherent in traditional single-node setups.

Furthermore, PAI-EAS now features a prefill-decode disaggregation function designed to boost performance and reduce operational costs.

Alibaba Cloud reported impressive results when deploying this with the Qwen2.5-72B model, achieving a 92% increase in concurrency and a 91% boost in tokens per second (TPS).

The PAI-Model Gallery has also been refreshed, now offering nearly 300 open-source models—including the complete range of Alibaba Cloud’s own open-source Qwen and Wan series. These are accessible via a no-code deployment and management interface.

Additional new PAI-Model Gallery features – like model evaluation and model distillation (transferring knowledge from large to smaller, more cost-effective models) – further enhance its utility.

Alibaba integrates AI into data management

Alibaba Cloud’s flagship cloud-native relational database, PolarDB, now incorporates native AI inference powered by Qwen.

PolarDB’s in-database machine learning capability eliminates the need to move data for inference workflows, which significantly cuts processing latency while improving efficiency and data security.

The feature is optimised for text-centric tasks such as developing conversational Retrieval-Augmented Generation (RAG) agents, generating text embeddings, and performing semantic similarity searches.

Additionally, the company’s data warehouse, AnalyticDB, is now integrated into Alibaba Cloud’s generative AI development platform Model Studio.

This integration serves as the recommended vector database for RAG solutions. This allows organisations to connect their proprietary knowledge bases directly with AI models on the platform to streamline the creation of context-aware applications.

New SaaS tools for industry transformation

Beyond infrastructure and platform layers, Alibaba Cloud introduced two new SaaS AI tools:

• AI Doc: An intelligent document processing tool using LLMs to parse diverse documents (reports, forms, manuals) efficiently. It extracts specific information and can generate tailored reports, such as ESG reports when integrated with Alibaba Cloud’s Energy Expert sustainability solution.
• Smart Studio: An AI-powered content creation platform supporting text-to-image, image-to-image, and text-to-video generation. It aims to enhance marketing and creative outputs in sectors like e-commerce, gaming, and entertainment, enabling features like virtual try-ons or generating visuals from text descriptions.

All these developments follow Alibaba’s announcement in February of a $53 billion investment over the next three years dedicated to advancing its cloud computing and AI infrastructure.

This colossal investment, noted as exceeding the company’s total AI and cloud expenditure over the previous decade, highlights a deep commitment to AI-driven growth and solidifies its position as a major global cloud provider.

“As cloud and AI become essential for global growth, we are committed to enhancing our core product offerings to address our customers’ evolving needs,” concludes Yuan.

See also: Amazon Nova Act: A step towards smarter, web-native AI agents

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The Qwen team have successfully integrated agent capabilities into the reasoning model, enabling it to think critically, utilise tools, and adapt its reasoning based on environmental feedback.

“Scaling RL has the potential to enhance model performance beyond conventional pretraining and post-training methods,” the team stated. “Recent studies have demonstrated that RL can significantly improve the reasoning capabilities of models.”

QwQ-32B achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated), a testament to the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge. This remarkable outcome underscores the potential of RL to bridge the gap between model size and performance.

The model has been evaluated across a range of benchmarks, including AIME24, LiveCodeBench, LiveBench, IFEval, and BFCL, designed to assess its mathematical reasoning, coding proficiency, and general problem-solving capabilities.

The results highlight QwQ-32B’s performance in comparison to other leading models, including DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the original DeepSeek-R1.

Benchmark results:

• AIME24: QwQ-32B achieved 79.5, slightly behind DeepSeek-R1-6718’s 79.8, but significantly ahead of OpenAl-o1-mini’s 63.6 and the distilled models.
• LiveCodeBench: QwQ-32B scored 63.4, again closely matched by DeepSeek-R1-6718’s 65.9, and surpassing the distilled models and OpenAl-o1-mini’s 53.8.
• LiveBench: QwQ-32B achieved 73.1, with DeepSeek-R1-6718 scoring 71.6, and outperforming the distilled models and OpenAl-o1-mini’s 57.5.
• IFEval: QwQ-32B scored 83.9, very close to DeepSeek-R1-6718’s 83.3, and leading the distilled models and OpenAl-o1-mini’s 59.1.
• BFCL: QwQ-32B achieved 66.4, with DeepSeek-R1-6718 scoring 62.8, demonstrating a lead over the distilled models and OpenAl-o1-mini’s 49.3.

The Qwen team’s approach involved a cold-start checkpoint and a multi-stage RL process driven by outcome-based rewards. The initial stage focused on scaling RL for math and coding tasks, utilising accuracy verifiers and code execution servers. The second stage expanded to general capabilities, incorporating rewards from general reward models and rule-based verifiers.

“We find that this stage of RL training with a small amount of steps can increase the performance of other general capabilities, such as instruction following, alignment with human preference, and agent performance, without significant performance drop in math and coding,” the team explained.

QwQ-32B is open-weight and available on Hugging Face and ModelScope under the Apache 2.0 license, and is also accessible via Qwen Chat. The Qwen team views this as an initial step in scaling RL to enhance reasoning capabilities and aims to further explore the integration of agents with RL for long-horizon reasoning.

“As we work towards developing the next generation of Qwen, we are confident that combining stronger foundation models with RL powered by scaled computational resources will propel us closer to achieving Artificial General Intelligence (AGI),” the team stated.

See also: Deepgram Nova-3 Medical: AI speech model cuts healthcare transcription errors

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Qwen 2.5-Max boasts pretraining on over 20 trillion tokens and fine-tuning through cutting-edge techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).

With the API now available through Alibaba Cloud and the model accessible for exploration via Qwen Chat, the Chinese tech giant is inviting developers and researchers to see its breakthroughs firsthand.

Outperforming peers  

When comparing Qwen 2.5-Max’s performance against some of the most prominent AI models on a variety of benchmarks, the results are promising.

Evaluations included popular metrics like the MMLU-Pro for college-level problem-solving, LiveCodeBench for coding expertise, LiveBench for overall capabilities, and Arena-Hard for assessing models against human preferences.

According to Alibaba, “Qwen 2.5-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.”

The instruct model – designed for downstream tasks like chat and coding – competes directly with leading models such as GPT-4o, Claude-3.5-Sonnet, and DeepSeek V3. Among these, Qwen 2.5-Max managed to outperform rivals in several key areas.

Comparisons of base models also yielded promising outcomes. While proprietary models like GPT-4o and Claude-3.5-Sonnet remained out of reach due to access restrictions, Qwen 2.5-Max was assessed against leading public options such as DeepSeek V3, Llama-3.1-405B (the largest open-weight dense model), and Qwen2.5-72B. Again, Alibaba’s newcomer demonstrated exceptional performance across the board.

“Our base models have demonstrated significant advantages across most benchmarks,” Alibaba stated, “and we are optimistic that advancements in post-training techniques will elevate the next version of Qwen 2.5-Max to new heights.”

Making Qwen 2.5-Max accessible  

To make the model more accessible to the global community, Alibaba has integrated Qwen 2.5-Max with its Qwen Chat platform, where users can interact directly with the model in various capacities—whether exploring its search capabilities or testing its understanding of complex queries.  

For developers, the Qwen 2.5-Max API is now available through Alibaba Cloud under the model name “qwen-max-2025-01-25”. Interested users can get started by registering an Alibaba Cloud account, activating the Model Studio service, and generating an API key.  

The API is even compatible with OpenAI’s ecosystem, making integration straightforward for existing projects and workflows. This compatibility lowers the barrier for those eager to test their applications with the model’s capabilities.

Alibaba has made a strong statement of intent with Qwen 2.5-Max. The company’s ongoing commitment to scaling AI models is not just about improving performance benchmarks but also about enhancing the fundamental thinking and reasoning abilities of these systems.  

“The scaling of data and model size not only showcases advancements in model intelligence but also reflects our unwavering commitment to pioneering research,” Alibaba noted.  

Looking ahead, the team aims to push the boundaries of reinforcement learning to foster even more advanced reasoning skills. This, they say, could enable their models to not only match but surpass human intelligence in solving intricate problems.  

The implications for the industry could be profound. As scaling methods improve and Qwen models break new ground, we are likely to see further ripples across AI-driven fields globally that we’ve seen in recent weeks.

(Photo by Maico Amorim)

See also: ChatGPT Gov aims to modernise US government agencies

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The Chinese tech giant’s cloud division has outlined several new initiatives, including an AI partner accelerator programme, enhanced incentives, and a refreshed global strategy for service partners, as it seeks to strengthen its position in the market.

Selina Yuan, President of International Business at Alibaba Cloud Intelligence, said: “At Alibaba Cloud, we believe that collaboration is the key to unlocking innovation and driving growth. Our global partners are not just participants, they are the architects of a new digital landscape in the AI era.

The company’s new AI Alliance Accelerator Programme aims to establish partnerships with 50 AI technology providers and 50 channel partners by 2025. Selected technology partners will receive enhanced technical support, expanded distribution channels, and dedicated AI consulting services, while channel partners will benefit from increased financial incentives for AI-related initiatives.

Alibaba Cloud has also introduced its Revitalised Service Partner Programme, designed to upskill existing partners and cultivate new ones through AI training and empowerment. The programme includes the joint development of Managed Large Language Model Services with service partners, leveraging the company’s generative AI capabilities.

The cloud provider has also committed to extending strategic partnerships with 18 service partners – including prominent names such as Deloitte, Accenture, and Cognizant Worldwide – from its existing pool of 50 global standard service partners.

In various regional developments, Alibaba Cloud has established strategic partnerships across Asia:

• Indonesia: The company has partnered with Telkom Indonesia to deliver AI-supported cloud solutions and develop digital talent.
• Japan: Information security firm Securai will localise Alibaba Cloud’s Zstack service for the Japanese market.
• Thailand: A memorandum of understanding with Yell Group aims to address growing demand for generative AI in the creative media industry.

The company, which currently maintains partnerships with approximately 12,000 organisations worldwide – including industry leaders such as Salesforce, Fortinet, IBM, and Neo4j – has introduced a Synergistic Incentive Programme to foster collaboration between its global technology and channel partners.

“Today, with our revamped global partner ecosystem, we are committed to supporting our global partners to jointly reap the benefits of the AI era and meet the diverse business demands of global customers,” Yuan concludes.

(Photo by Hannah Busing)

See also: Alibaba Marco-o1: Advancing LLM reasoning capabilities

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Marco-o1, from Alibaba’s MarcoPolo team, represents another step forward in the ability of AI to handle complex reasoning challenges—particularly in maths, physics, coding, and areas where clear standards may be absent.

Building upon OpenAI’s reasoning advancements with its o1 model, Marco-o1 distinguishes itself by incorporating several advanced techniques, including Chain-of-Thought (CoT) fine-tuning, Monte Carlo Tree Search (MCTS), and novel reflection mechanisms. These components work in concert to enhance the model’s problem-solving capabilities across various domains.

The development team has implemented a comprehensive fine-tuning strategy using multiple datasets, including a filtered version of the Open-O1 CoT Dataset, a synthetic Marco-o1 CoT Dataset, and a specialised Marco Instruction Dataset. In total, the training corpus comprises over 60,000 carefully curated samples.

The model has demonstrated particularly impressive results in multilingual applications. In testing, Marco-o1 achieved notable accuracy improvements of 6.17% on the English MGSM dataset and 5.60% on its Chinese counterpart. The model has shown particular strength in translation tasks, especially when handling colloquial expressions and cultural nuances.

One of the model’s most innovative features is its implementation of varying action granularities within the MCTS framework. This approach allows the model to explore reasoning paths at different levels of detail, from broad steps to more precise “mini-steps” of 32 or 64 tokens. The team has also introduced a reflection mechanism that prompts the model to self-evaluate and reconsider its reasoning, leading to improved accuracy in complex problem-solving scenarios.

The MCTS integration has proven particularly effective, with all MCTS-enhanced versions of the model showing significant improvements over the base Marco-o1-CoT version. The team’s experiments with different action granularities have revealed interesting patterns, though they note that determining the optimal strategy requires further research and more precise reward models.

The development team has been transparent about the model’s current limitations, acknowledging that while Marco-o1 exhibits strong reasoning characteristics, it still falls short of a fully realised “o1” model. They emphasise that this release represents an ongoing commitment to improvement rather than a finished product.

Looking ahead, the Alibaba team has announced plans to incorporate reward models, including Outcome Reward Modeling (ORM) and Process Reward Modeling (PRM), to enhance the decision-making capabilities og Marco-o1. They are also exploring reinforcement learning techniques to further refine the model’s problem-solving abilities.

The Marco-o1 model and associated datasets have been made available to the research community through Alibaba’s GitHub repository, complete with comprehensive documentation and implementation guides. The release includes installation instructions and example scripts for both direct model usage and deployment via FastAPI.

(Photo by Alina Grubnyak)

See also: New AI training techniques aim to overcome current challenges

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ModelScope, which embodies Alibaba Cloud’s concept of “Model-as-a-Service,” transforms AI models into readily available and deployable services. Since its launch in mainland China in 2022, the platform has grown to become the country’s largest AI model community, boasting over five million developer users.

With this international expansion, developers around the globe will now have access to more than 5,000 advanced AI models. The platform also welcomes user-contributed models, fostering a collaborative ecosystem for AI development.

The English version of ModelScope provides a comprehensive suite of tools and resources to support developers in bringing their AI projects to fruition. This includes access to over 1,500 high-quality Chinese-language datasets and an extensive range of toolkits for data processing. Moreover, the platform offers various modules that allow developers to customise model inference, training, and evaluation with minimal coding requirements.

Alibaba Cloud announced the English version of ModelScope during the 2024 Computer Vision and Pattern Recognition (CVPR) Conference in Seattle. This annual event brings together academics, researchers, and business leaders for a five-day exploration of cutting-edge developments in AI and machine learning through workshops, panels, and keynotes.

The company’s presence at CVPR was further bolstered by the acceptance of more than 30 papers from Alibaba Group, with six selected as oral and highlighted papers. This achievement underscores Alibaba’s commitment to advancing the field of AI research and development.

Conference attendees also had the opportunity to experience firsthand the capabilities of Alibaba’s proprietary Qwen model series at the company’s booth. The demonstration showcased the model’s impressive image and video generation capabilities, providing a glimpse into the potential applications of Alibaba’s AI technologies.

The launch of the English version of ModelScope represents a significant milestone in Alibaba Cloud’s strategy to expand its AI offerings globally.

As businesses and developers worldwide increasingly seek to harness the power of AI, platforms like ModelScope are set to play a crucial role in democratising access to advanced AI capabilities. With its extensive collection of models, datasets, and development tools, Alibaba Cloud’s ModelScope will help to accelerate AI innovation and adoption on a global scale.

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See also: SoftBank chief: Forget AGI, ASI will be here within 10 years

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Alibaba’s ChatGPT rival is called Tongyi Qianwen and will be integrated across the company’s various businesses in the “near future,” but it is yet to give a rollout timeline.

“We are at a technological watershed moment driven by generative AI and cloud computing, and businesses across all sectors have started to embrace intelligence transformation to stay ahead of the game,” said Daniel Zhang, Chairman and CEO of Alibaba Group and CEO of Alibaba Cloud Intelligence.

“As a leading global cloud computing service provider, Alibaba Cloud is committed to making computing and AI services more accessible and inclusive for enterprises and developers, enabling them to uncover more insights, explore new business models for growth, and create more cutting-edge products and services for society.”

Tongyi Qianwen roughly translates to “seeking an answer by asking a thousand questions” and will support both English and Chinese languages.

Alibaba has stated that the chatbot will first be added to DingTalk, its workplace messaging app. Tongyi Qianwen will be able to perform several tasks at launch, including taking notes in meetings, writing emails, and drafting business proposals.

The chatbot will be integrated into Tmall Genie, similar to Amazon’s line of Echo smart speakers. That integration will give Alibaba an advantage over its Western counterparts such as Google which are yet to integrate their own equivalents into their smart speakers. 

Tongyi Qianwen is powered by an LLM that reportedly consists of ten trillion parameters, which is significantly more than GPT-4 (estimated to consist of around one trillion parameters.)

The model will be used as the foundation for a new service by Alibaba that will see the company build custom LLMs for customers. The LLMs will use “customers’ proprietary intelligence and industrial know-how” to build AI-infused apps without developing a model from scratch. A beta version of a Tongyi Qianwen API is already available for Chinese developers.

“Generative AI powered by large language models is ushering in an unprecedented new phase. In this latest AI era, we can create additional value for our customers and broader communities through our resilient public cloud infrastructure and proven AI capabilities,” said Jingren Zhou, CTO of Alibaba Cloud Intelligence.

“We are witnessing a new paradigm of AI development where cloud and AI models play an essential role. By making this paradigm more inclusive, we hope to facilitate businesses from all industries with their intelligence transformation and, ultimately, help boost their business productivity and expand their expertise and capabilities while unlocking more exciting opportunities through innovations.”

Last month, a group of high-profile figures in the technology industry called for the suspension of training powerful AI systems. Twitter CEO Elon Musk and Apple co-founder Steve Wozniak were among those who signed an open letter warning of potential risks and said the race to develop AI systems is out of control.

A report by investment bank Goldman Sachs estimated that AI could replace the equivalent of 300 million full-time jobs. An AI think tank, meanwhile, called GPT-4 a risk to public safety.

Alibaba’s announcements were made at its Cloud Summit, which also featured the debut of three-month trials for its Infrastructure-as-a-Service (IaaS) and PolarDB services. The company is offering a 50 percent discount for its storage-as-a-service offering if users reserve capacity in a specific region for a year.

The company has not yet revealed the cost of using Tongyi Qianwen.

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The third largest cloud services company in China, following Alibaba and Huawei, Tencent recently revealed the three chips at its 2021 Digital Ecology Conference.

Current information on the three chips can be summarised as follows:

• Zixiao – an “AI reasoning” chip that supposedly offers 100 percent better performance than rival products. It combines image and video processing with natural language processing, search recommendations, and other features
• Xuangling – a SmartNIC or Data Processing Unit (DPU) that runs virtualisation of storage and networking for a cloud host’s CPU so that it doesn’t have to. Tencent claims this comes with zero cost to the host CPU and that it performs four times faster than similar industry products
• Canghai – a video transcoding chip that supposedly delivers a 30 percent improved compression rate over other on-market products. It achieves this through multi-core expansion architecture, a high-performance coding pipeline, and a hierarchical memory layout

Whilst these suggested improvements are substantial, the reliability of these figures cannot yet be accounted for.

Their development comes on the back of Tencent establishing a chip research and development lab in Penglai in 2020. Its goal of achieving full end-to-end coverage of Tencent’s chip design and verification appears to have been realised with the announcement.

Tang Daosheng, senior executive VP of Tencent, said at the conference: “Facing strong business needs, Tencent developed a long-term chip research and development investment plan. Currently, it has already implemented three directions with substantial progress.”

Tencent currently operates outside of Asia in the USA, Brazil, Germany, and Russia, with keen plans to expand further into Europe, the Americas, and Africa.

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

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The AI-powered tool is able to analyse CT scans for signs of COVID-19 infection to help quickly diagnose the novel coronavirus which has caused havoc around the world.

With heroic medical staff under more pressure than ever caring for the huge influx of people suffering with COVID-19 – in addition to all the other ailments they have to treat – such an AI-powered tool could help to free up significant amounts of time.

M3 has been testing the solution in Japan since the end of March; with the aim of deploying it across hundreds of locations. 

Hospitals will send CT scans to M3’s system which will then return the results with a 1-5 scale indicating the likelihood of COVID-19 pneumonia.

Alibaba’s system has been used in Chinese hospitals – including in Wuhan, the expected source of the COVID-19 outbreak – for a while now. The Chinese tech giant claims its AI can diagnose COVID-19 within 20 seconds with an accuracy of 90 percent or higher.

On average, a doctor takes around 20 minutes to make a diagnosis once a CT scan is available. M3 has found that the system typically diagnoses in under a minute.

While finding the accuracy to be relatively high, M3 reports the accuracy falls short of the 90 percent claimed by Alibaba. Even at 90 percent, 100 patients in every 1000 risk being misdiagnosed.

However, reading COVID-19 scans is reportedly even tricky for skilled physicians – especially as the virus is still relatively new. An AI-powered system which frees up clinical time is sure to be welcomed by all hospitals.

Catching the smaller signs of COVID-19 early could even help with providing treatment to those who need it before they get seriously ill.

This isn’t the first time AI has been looked to for assistance in tackling the COVID-19 pandemic.

Earlier this week, researchers from WVU Medicine and the Rockefeller Neuroscience Institute said they were able to predict the onset of COVID-19 symptoms three days early using AI to analyse data from Oura’s wearable rings.

Back in April, researchers from Carnegie Mellon University launched an AI-powered voice analysis system which aims to determine whether someone is suffering from COVID-19 using just a website.

While it seems likely we’re going to be living with COVID-19 in our lives for the foreseeable future, AI technologies look ready to step in and help.

(Photo by Robina Weermeijer on Unsplash)

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The neural processing unit is already being used to power features on Alibaba’s e-commerce sites, including product search and personalised recommendations. The Hanguang 800 will be made available to Alibaba Cloud customers at a later stage.

According to Alibaba, its ecommerce website Taobao previously took an hour to categorise one billion product images that are uploaded to the site each day by merchants and prepare them for search and personalised recommendations. However, with the Hanguang 800, Taobao was able to finish the task in just five minutes.  

Apart from using the Hanguang 800 in many business operations, the chip is also used for automatic translation on its e-commerce sites, advertising, and intelligent customer services.

Jeff Zhang, Alibaba Group CTO and president of Alibaba Cloud Intelligence, said:

“The launch of Hanguang 800 is an important step in our pursuit of next-generation technologies, boosting computing capabilities that will drive both our current and emerging businesses while improving energy-efficiency. In the near future, we plan to empower our clients by providing access through our cloud business to the advanced computing that is made possible by the chip, anytime and anywhere.”

The Hanguang 800 was created by T-Head, the unit that leads the development of chips for cloud and edge computing within Alibaba DAMO Academy. The unit has developed the chip’s hardware and algorithms designed for business apps, including Alibaba’s retail and logistics apps.

It is believed that the Hanguang 800 would help Chinese companies to reduce their dependence on American technology, as the trade war makes business collaborations between the two countries more and more difficult.

The company has not revealed any details on the availability of the chip so far.

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