The companies’ efforts follow the announcement of OpenAI’s latest LLMs, including the o1 generative pre-trained transformer model backed by Microsoft. The model is intended to tackle difficult tasks, paving the way for advances in science, coding, and mathematics.

During the conference, Kunal Zhilin, founder of Moonshot AI, underlined the importance of the o1 model, adding that it has the potential to reshape various industries and create new opportunities for AI startups.

Zhilin stated that reinforcement learning and scalability might be pivotal for AI development. He spoke of the scaling law, which states that larger models with more training data perform better.

“This approach pushes the ceiling of AI capabilities,” Zhilin said, adding that OpenAI o1 has the potential to disrupt sectors and generate new opportunities for startups.

OpenAI has also stressed the model’s ability to solve complex problems, which it says operate in a manner similar to human thinking. By refining its strategies and learning from mistakes, the model improves its problem-solving capabilities.

Zhilin said companies with enough computing power will be able to innovate not only in algorithms, but also in foundational AI models. He sees this as pivotal, as AI engineers rely increasingly on reinforcement learning to generate new data after exhausting available organic data sources.

StepFun CEO Jiang Daxin concurred with Zhilin but stated that computational power remains a big challenge for many start-ups, particularly due to US trade restrictions that hinder Chinese enterprises’ access to advanced semiconductors.

“The computational requirements are still substantial,” Daxin stated.

An insider at Baichuan AI has said that only a small group of Chinese AI start-ups — including Moonshot AI, Baichuan AI, Zhipu AI, and MiniMax — are in a position to make large-scale investments in reinforcement learning. These companies — collectively referred to as the “AI tigers” — are involved heavily in LLM development, pushing the next generation of AI.

More from the Apsara Conference

Also at the conference, Alibaba Cloud made several announcements, including the release of its Qwen 2.5 model family, which features advances in coding and mathematics. The models range from 0.5 billion to 72 billion parameters and support approximately 29 languages, including Chinese, English, French, and Spanish.

Specialised models such as Qwen2.5-Coder and Qwen2.5-Math have already gained some traction, with over 40 million downloads on platforms Hugging Face and ModelScope.

Alibaba Cloud added to its product portfolio, delivering a text-to-video model in its picture generator, Tongyi Wanxiang. The model can create videos in realistic and animated styles, with possible uses in advertising and filmmaking.

Alibaba Cloud unveiled Qwen 2-VL, the latest version of its vision language model. It handles videos longer than 20 minutes, supports video-based question-answering, and is optimised for mobile devices and robotics.

For more information on the conference, click here.

(Photo by: @Guy_AI_Wise via X)

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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MCP works like a universal connector that lets AI assistants interact directly with favourite apps and services – enabling them to make payments, book appointments, check maps, and access information on different platforms on users’ behalves.

As reported by the South China Morning Post, companies like Ant Group, Alibaba Cloud, and Baidu are deploying MCP-based services and positioning AI agents as the next step, after chatbots and large language models. But will China’s MCP adoption truly transform the AI landscape, or is it simply another step in the technology’s evolution?

Why China’s MCP adoption matters for AI’s evolution

The Model Context Protocol was initially introduced by Anthropic in November 2024, at the time described as a standard that connects AI agents “to the systems where data lives, including content repositories, business tools and development environments.”

MCP serves as what Ant Group calls a “USB-C port for AI applications” – a universal connector allowing AI agents to integrate with multiple systems.

The standardisation is particularly significant for AI agents like Butterfly Effect’s Manus, which are designed to autonomously perform tasks by creating plans consisting of specific subtasks using available resources.

Unlike traditional chatbots that just respond to queries, AI agents can actively interact with different systems, collect feedback, and incorporate that feedback into new actions.

Chinese tech giants lead the MCP movement

China’s MCP adoption by tech leaders highlights the importance placed on AI agents as the next evolution in artificial intelligence:

• Ant Group, Alibaba’s fintech affiliate, has unveiled its “MCP server for payment services,” that lets AI agents connect with Alipay’s payment platform. The integration allows users to “easily make payments, check payment statuses and initiate refunds using simple natural language commands,” according to Ant Group’s statement.
• Additionally, Ant Group’s AI agent development platform, Tbox, now supports deployment of more than 30 MCP services currently on the market, including those for Alipay, Amap Maps, Google MCP, and Amazon Web Services’ knowledge base retrieval server.
• Alibaba Cloud launched an MCP marketplace through its AI model hosting platform ModelScope, offering more than 1,000 services connecting to mapping tools, office collaboration platforms, online storage services, and various Google services.
• Baidu, China’s leading search and AI company, has indicated that its support for MCP would foster “abundant use cases for [AI] applications and solutions.”

Beyond chatbots: Why AI agents represent the next frontier

China’s MCP adoption signals a shift in focus from large language models and chatbots to more capable AI agents. As Red Xiao Hong, founder and CEO of Butterfly Effect, described, an AI agent is “more like a human being” compared to how chatbots perform.

The agents not only respond to questions but “interact with the environment, collect feedback and use the feedback as a new prompt.” This distinction is held to be important by companies driving progress in AI.

While chatbots and LLMs can generate text and respond to queries, AI agents can take actions on multiple platforms and services. They represent an advance from the limited capabilities of conventional AI applications toward autonomous systems capable of completing more complex tasks with less human intervention.

The rapid embrace of MCP by Chinese tech companies suggests they view AI agents as a new avenue for innovation and commercial opportunity that go beyond what’s possible with existing chatbots and language models.

China’s MCP adoption could position its tech companies at the forefront of practical AI implementation. By creating standardised ways for AI agents to interact with services, Chinese companies are building ecosystems where AI could deliver more comprehensive experiences.

Challenges and considerations of China’s MCP adoption

Despite the developments in China’s MCP adoption, several factors may influence the standard’s longer-term impact:

1. International standards competition. While Chinese tech companies are racing to implement MCP, its global success depends on widespread adoption. Originally developed by Anthropic, the protocol faces potential competition from alternative standards that might emerge from other major AI players like OpenAI, Google, or Microsoft.
2. Regulatory environments. As AI agents gain more autonomy in performing tasks, especially those involving payments and sensitive user data, regulatory scrutiny will inevitably increase. China’s regulatory landscape for AI is still evolving, and how authorities respond to these advancements will significantly impact MCP’s trajectory.
3. Security and privacy. The integration of AI agents with multiple systems via MCP creates new potential vulnerabilities. Ensuring robust security measures across all connected platforms will be important for maintaining user trust.
4. Technical integration challenges. While the concept of universal connectivity is appealing, achieving integration across diverse systems with varying architectures, data structures, and security protocols presents significant technical challenges.

The outlook for China’s AI ecosystem

China’s MCP adoption represents a strategic bet on AI agents as the next evolution in artificial intelligence. If successful, it could accelerate the practical implementation of AI in everyday applications, potentially transforming how users interact with digital services.

As Red Xiao Hong noted, AI agents are designed to interact with their environment in ways that more closely resemble human behaviour than traditional AI applications. The capacity for interaction and adaptation could be what finally bridges the gap between narrow AI tools and the more generalised assistants that tech companies have long promised.

See also: Manus AI agent: breakthrough in China’s agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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If the performance claims prove accurate, the AI hardware breakthrough might reshape the technology landscape at a time when AI development is continuing worldwide, and despite US efforts to limit China’s access to advanced technology.

300 petaflops: Challenging Nvidia’s hardware dominance

The CloudMatrix 384 Supernode is described as a “nuclear-level product,” according to reports from STAR Market Daily cited by the South China Morning Post (SCMP). The hardware achieves an impressive 300 petaflops of computing power, in excess of the 180 petaflops delivered by Nvidia’s NVL72 system.

The CloudMatrix 384 Supernode was specifically engineered to address the computing bottlenecks that have become increasingly problematic as artificial intelligence models continue to grow in size and complexity.

The system is designed to compete directly with Nvidia’s offerings, which have dominated the global market for AI accelerator hardware thus far. Huawei’s CloudMatrix infrastructure was first unveiled in September 2024, and was developed specifically to meet surging demand in China’s domestic market.

The 384 Supernode variant represents the most powerful implementation of AI architecture to date, with reports indicating it can achieve a throughput of 1,920 tokens per second and maintain high levels of accuracy, reportedly matching the performance of Nvidia’s H100 chips, but using Chinese-made components instead.

Developing under sanctions: The technical achievement

What makes the AI hardware breakthrough particularly significant is that it has been achieved despite the severe technological restrictions Huawei has faced since being placed on the US Entity List.

Sanctions have limited the company’s access to advanced US semiconductor technology and design software, forcing Huawei to develop alternative approaches and rely on domestic supply chains.

The core technological advancement enabling the CloudMatrix 384’s performance appears to be Huawei’s answer to Nvidia’s NVLink – a high-speed interconnect technology that allows multiple GPUs to communicate efficiently.

Nvidia’s NVL72 system, released in March 2024, features a 72-GPU NVLink domain that functions as a single, powerful GPU, enabling real-time inference for trillion-parameter models at speeds 30 times faster than previous generations.

According to reporting from the SCMP, Huawei is collaborating with Chinese AI infrastructure startup SiliconFlow to implement the CloudMatrix 384 Supernode in supporting DeepSeek-R1, a reasoning model from Hangzhou-based DeepSeek.

Supernodes are AI infrastructure architectures equipped with more resources than standard systems – including enhanced central processing units, neural processing units, network bandwidth, storage, and memory.

The configuration allows them to function as relay servers, enhancing the overall computing performance of clusters and significantly accelerating the training of foundational AI models.

Beyond Huawei: China’s broader AI infrastructure push

The AI hardware breakthrough from Huawei doesn’t exist in isolation but rather represents part of a broader push by Chinese technology companies to build domestic AI computing infrastructure.

In February, e-commerce giant Alibaba Group announced a massive 380 billion yuan ($52.4 billion) investment in computing resources and AI infrastructure over three years – the largest-ever investment by a private Chinese company in a computing project.

For the global AI community, the emergence of viable alternatives to Nvidia’s hardware could eventually address the computing bottlenecks that have limited AI advancement. Competition in this space could potentially increase available computing capacity and provide developers with more options for training and deploying their models.

However, it’s worth noting that as of the report’s publication, Huawei had not yet responded to requests for comment on these claims.

As tensions between the US and China continue to intensify in the technology sector, Huawei’s CloudMatrix 384 Supernode represents a significant development in China’s pursuit of technological self-sufficiency.

If the performance claims are verified, this AI hardware breakthrough would mean Huawei has achieved computing independence in this niche, despite facing extensive sanctions.

The development also signals a broader trend in China’s technology sector, with multiple domestic companies intensifying their investments in AI infrastructure to capitalise on growing demand and promote the adoption of homegrown chips.

The collective effort suggests China is committed to developing domestic alternatives to American technology in this strategically important field..

See also: Manus AI agent: breakthrough in China’s agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post Huawei’s AI hardware breakthrough challenges Nvidia’s dominance appeared first on AI News.

The San Francisco-based company, which states its mission is “building general superintelligence,” has launched preview versions of LLMs in 3B, 8B, 14B, 32B, and 70B parameter sizes. Deep Cogito asserts that “each model outperforms the best available open models of the same size, including counterparts from LLAMA, DeepSeek, and Qwen, across most standard benchmarks”.

Impressively, the 70B model from Deep Cogito even surpasses the performance of the recently released Llama 4 109B Mixture-of-Experts (MoE) model.   

Iterated Distillation and Amplification (IDA)

Central to this release is a novel training methodology called Iterated Distillation and Amplification (IDA). 

Deep Cogito describes IDA as “a scalable and efficient alignment strategy for general superintelligence using iterative self-improvement”. This technique aims to overcome the inherent limitations of current LLM training paradigms, where model intelligence is often capped by the capabilities of larger “overseer” models or human curators.

The IDA process involves two key steps iterated repeatedly:

• Amplification: Using more computation to enable the model to derive better solutions or capabilities, akin to advanced reasoning techniques.
• Distillation: Internalising these amplified capabilities back into the model’s parameters.

Deep Cogito says this creates a “positive feedback loop” where model intelligence scales more directly with computational resources and the efficiency of the IDA process, rather than being strictly bounded by overseer intelligence.

“When we study superintelligent systems,” the research notes, referencing successes like AlphaGo, “we find two key ingredients enabled this breakthrough: Advanced Reasoning and Iterative Self-Improvement”. IDA is presented as a way to integrate both into LLM training.

Deep Cogito claims IDA is efficient, stating the new models were developed by a small team in approximately 75 days. They also highlight IDA’s potential scalability compared to methods like Reinforcement Learning from Human Feedback (RLHF) or standard distillation from larger models.

As evidence, the company points to their 70B model outperforming Llama 3.3 70B (distilled from a 405B model) and Llama 4 Scout 109B (distilled from a 2T parameter model).

Capabilities and performance of Deep Cogito models

The newly released Cogito models – based on Llama and Qwen checkpoints – are optimised for coding, function calling, and agentic use cases.

A key feature is their dual functionality: “Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models),” similar to capabilities seen in models like Claude 3.5. However, Deep Cogito notes they “have not optimised for very long reasoning chains,” citing user preference for faster answers and the efficiency of distilling shorter chains.

Extensive benchmark results are provided, comparing Cogito models against size-equivalent state-of-the-art open models in both direct (standard) and reasoning modes.

Across various benchmarks (MMLU, MMLU-Pro, ARC, GSM8K, MATH, etc.) and model sizes (3B, 8B, 14B, 32B, 70B,) the Cogito models generally show significant performance gains over counterparts like Llama 3.1/3.2/3.3 and Qwen 2.5, particularly in reasoning mode.

For instance, the Cogito 70B model achieves 91.73% on MMLU in standard mode (+6.40% vs Llama 3.3 70B) and 91.00% in thinking mode (+4.40% vs Deepseek R1 Distill 70B). Livebench scores also show improvements.

Here are benchmarks of 14B models for a medium-sized comparison:

While acknowledging benchmarks don’t fully capture real-world utility, Deep Cogito expresses confidence in practical performance.

This release is labelled a preview, with Deep Cogito stating they are “still in the early stages of this scaling curve”. They plan to release improved checkpoints for the current sizes and introduce larger MoE models (109B, 400B, 671B) “in the coming weeks / months”. All future models will also be open-source.

(Photo by Pietro Mattia)

See also: Alibaba Cloud targets global AI growth with new models and tools

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post Deep Cogito open LLMs use IDA to outperform same size models appeared first on AI News.

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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The Alibaba-owned company has used chips from domestic suppliers, including those tied to its parent, Alibaba, and Huawei Technologies to train large language models using the Mixture of Experts (MoE) method. The results were reportedly comparable to those produced with Nvidia’s H800 chips, sources claim. While Ant continues to use Nvidia chips for some of its AI development, one sources said the company is turning increasingly to alternatives from AMD and Chinese chip-makers for its latest models.

The development signals Ant’s deeper involvement in the growing AI race between Chinese and US tech firms, particularly as companies look for cost-effective ways to train models. The experimentation with domestic hardware reflects a broader effort among Chinese firms to work around export restrictions that block access to high-end chips like Nvidia’s H800, which, although not the most advanced, is still one of the more powerful GPUs available to Chinese organisations.

Ant has published a research paper describing its work, stating that its models, in some tests, performed better than those developed by Meta. Bloomberg News, which initially reported the matter, has not verified the company’s results independently. If the models perform as claimed, Ant’s efforts may represent a step forward in China’s attempt to lower the cost of running AI applications and reduce the reliance on foreign hardware.

MoE models divide tasks into smaller data sets handled by separate components, and have gained attention among AI researchers and data scientists. The technique has been used by Google and the Hangzhou-based startup, DeepSeek. The MoE concept is similar to having a team of specialists, each handling part of a task to make the process of producing models more efficient. Ant has declined to comment on its work with respect to its hardware sources.

Training MoE models depends on high-performance GPUs which can be too expensive for smaller companies to acquire or use. Ant’s research focused on reducing that cost barrier. The paper’s title is suffixed with a clear objective: Scaling Models “without premium GPUs.” [our quotation marks]

The direction taken by Ant and the use of MoE to reduce training costs contrast with Nvidia’s approach. CEO Officer Jensen Huang has said that demand for computing power will continue to grow, even with the introduction of more efficient models like DeepSeek’s R1. His view is that companies will seek more powerful chips to drive revenue growth, rather than aiming to cut costs with cheaper alternatives. Nvidia’s strategy remains focused on building GPUs with more cores, transistors, and memory.

According to the Ant Group paper, training one trillion tokens – the basic units of data AI models use to learn – cost about 6.35 million yuan (roughly $880,000) using conventional high-performance hardware. The company’s optimised training method reduced that cost to around 5.1 million yuan by using lower-specification chips.

Ant said it plans to apply its models produced in this way – Ling-Plus and Ling-Lite – to industrial AI use cases like healthcare and finance. Earlier this year, the company acquired Haodf.com, a Chinese online medical platform, to further Ant’s ambition to deploy AI-based solutions in healthcare. It also operates other AI services, including a virtual assistant app called Zhixiaobao and a financial advisory platform known as Maxiaocai.

“If you find one point of attack to beat the world’s best kung fu master, you can still say you beat them, which is why real-world application is important,” said Robin Yu, chief technology officer of Beijing-based AI firm, Shengshang Tech.

Ant has made its models open source. Ling-Lite has 16.8 billion parameters – settings that help determine how a model functions – while Ling-Plus has 290 billion. For comparison, estimates suggest closed-source GPT-4.5 has around 1.8 trillion parameters, according to MIT Technology Review.

Despite progress, Ant’s paper noted that training models remains challenging. Small adjustments to hardware or model structure during model training sometimes resulted in unstable performance, including spikes in error rates.

(Photo by Unsplash)

See also: DeepSeek V3-0324 tops non-reasoning AI models in open-source first

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The new model advanced seven points in the benchmark to surpass proprietary counterparts such as Google’s Gemini 2.0 Pro, Anthropic’s Claude 3.7 Sonnet, and Meta’s Llama 3.3 70B.

While V3-0324 trails behind reasoning models, including DeepSeek’s own R1 and offerings from OpenAI and Alibaba, the achievement highlights the growing viability of open-source solutions in latency-sensitive applications where immediate responses are critical.

DeepSeek V3-0324 represents a new era for open-source AI

Non-reasoning models – which generate answers instantly without deliberative “thinking” phases – are essential for real-time use cases like chatbots, customer service automation, and live translation. DeepSeek’s latest iteration now sets the standard for these applications, eclipsing even leading proprietary tools.

“This is the first time an open weights model is the leading non-reasoning model, a milestone for open source,” states Artificial Analysis. The model’s performance edges it closer to proprietary reasoning models, though the latter remain superior for tasks requiring complex problem-solving.

DeepSeek V3-0324 retains most specifications from its December 2024 predecessor, including:  

• 128k context window (capped at 64k via DeepSeek’s API)
• 671 billion total parameters, necessitating over 700GB of GPU memory for FP8 precision
• 37 billion active parameters
• Text-only functionality (no multimodal support) 
• MIT License

“Still not something you can run at home!” Artificial Analysis quips, emphasising its enterprise-grade infrastructure requirements.

Open-source AI is bringing the heat

While proprietary reasoning models like DeepSeek R1 maintain dominance in the broader Intelligence Index, the gap is narrowing.

Three months ago, DeepSeek V3 nearly matched Anthropic’s and Google’s proprietary models but fell short of surpassing them. Today, the updated V3-0324 not only leads open-source alternatives but also outperforms all proprietary non-reasoning rivals.

“This release is arguably even more impressive than R1,” says Artificial Analysis.

DeepSeek’s progress signals a shift in the AI sector, where open-source frameworks increasingly compete with closed systems. For developers and enterprises, the MIT-licensed V3-0324 offers a powerful, adaptable tool—though its computational costs may limit accessibility.

“DeepSeek are now driving the frontier of non-reasoning open weights models,” declares Artificial Analysis.

With R2 on the horizon, the community awaits another potential leap in AI performance.

(Photo by Paul Hanaoka)

See also: Hugging Face calls for open-source focus in the AI Action Plan

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post DeepSeek V3-0324 tops non-reasoning AI models in open-source first appeared first on AI News.

Unlike anything seen to date, the Manus AI agent doesn’t just chat with users – it is allegedly capable of independently tackling complex multi-step tasks with minimal human guidance.

Developed by Chinese startup Butterfly Effect with financial backing from tech giant Tencent Holdings, Manus AI agent has captured global attention for its ability to bridge the gap between theoretical AI capabilities and practical, real-world applications. It uses an innovative multi-model architecture that combines the strengths of multiple leading language models.

Breakthrough autonomous task execution

In a post on X, Peak Ji Yichao, co-founder and chief scientist at Butterfly Effect, said that the agentic AI was built using existing large language models, including Anthropic’s Claude and fine-tuned versions of Alibaba’s open-source Qwen.

Its multi-model nature allows Manus to use different AI strengths according to what’s demanded of it, resulting in more sophisticated reasoning and execution capabilities.

“The Manus AI agent represents a fundamentally different approach to artificial intelligence,” CNN Business stated. According to coverage, Manus “can carry out complex, multi-step tasks like screening resumés and creating a website,” and “doesn’t only generate ideas but delivers tangible results, like producing a report recommending properties to buy based on specific criteria.”

Real-world performance assessment

In an extensive hands-on evaluation, MIT Technology Review tested the Manus AI agent in three distinct task categories: compiling comprehensive journalist lists, conducting real estate searches with complex parameters, and identifying candidates for its prestigious Innovators Under 35 program.

“Using Manus feels like collaborating with a highly intelligent and efficient intern,” wrote Caiwei Chen in the assessment. “While it occasionally lacks understanding of what it’s being asked to do, makes incorrect assumptions, or cuts corners to expedite tasks, it explains its reasoning clearly, is remarkably adaptable, and can improve substantially when provided with detailed instructions or feedback.”

The evaluation revealed one of the Manus AI agent’s most distinctive features – its “Manus’s Computer” interface, which provides unprecedented transparency into the AI’s decision-making process.

The application window lets users observe the agent’s actions in real time and intervene when necessary, creating a collaborative human-AI workflow that maintains user control while automating complex processes.

Technical implementation challenges

Despite impressive capabilities, the Manus AI agent faces significant technical hurdles in its current implementation.MIT Technology Reviewdocumented frequent system crashes and timeout errors during extended use.

The platform displayed error messages, citing “high service load,” suggesting that computational infrastructure remains a limitation.

The technical constraints have contributed to highly restricted access, with less than 1% of wait-listed users receiving invite codes – the official Manus Discord channel has already accumulated over 186,000 members.

According to reporting from Chinese technology publication36Kr, the Manus AI agent’s operational costs remain relatively competitive at approximately $2 per task.

Strategic partnership with Alibaba Cloud

The creators of the Manus AI agent have announced a partnership with Alibaba’s cloud computing division. According to a South China Morning Post report dated March 11, “Manus will engage in strategic cooperation with Alibaba’s Qwen team to meet the needs of Chinese users.”

The partnership aims to make Manus available on “domestic models and computing platforms,” although implementation timelines remain unspecified.

Parallel advancements in foundation models

The Manus-Alibaba partnership coincides with Alibaba’s advances in AI foundation model technology. On March 6, the company published its QwQ-32B reasoning model, claiming performance characteristics that surpass OpenAI’s o1-mini and rivalling DeepSeek’s R1 model, despite a lower parameter count.

CNN Businessreported, “Alibaba touted its new model, QwQ-32B, in an online statement as delivering exceptional performance, almost entirely surpassing OpenAI-o1-mini and rivalling the strongest open-source reasoning model, DeepSeek-R1.”

The claimed efficiency gains are particularly noteworthy – Alibaba says QwQ-32B achieves competitive performance with just 32 billion parameters, compared to the 671 billion parameters in DeepSeek’s R1 model. The reduced model size suggests substantially lower computational requirements for training and inference with advanced reasoning capabilities.

China’s strategic AI investments

The Manus AI agent and Alibaba’s model advancements reflect China’s broader strategic emphasis on artificial intelligence development. The Chinese government has pledged explicit support for “emerging industries and industries of the future,” with artificial intelligence receiving particular focus alongside quantum computing and robotics.

Alibaba will invest 380 billion yuan (approximately $52.4 billion) in AI and cloud computing infrastructure in the next three years, a figure the company notes exceeds its total investments in these sectors during the previous decade.

As MIT Technology Review’s Caiwei Chen said, “Chinese AI companies are not just following in the footsteps of their Western counterparts. Rather than just innovating on base models, they are actively shaping the adoption of autonomous AI agents in their way.”

The Manus AI agent also exemplifies how China’s artificial intelligence ecosystem has evolved beyond merely replicating Western advances. Government policies promoting technological self-reliance, substantial funding initiatives, and a growing pipeline of specialised AI talent from Chinese universities have created conditions for original innovation.

Rather than a single approach to artificial intelligence, we are witnessing diverse implementation philosophies likely resulting in complementary systems optimised for different uses and cultural contexts.

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Built upon the foundations of the company’s Gemini 2.0 models, Gemma 3 is engineered to be lightweight, portable, and adaptable—enabling developers to create AI applications across a wide range of devices.  

This release comes hot on the heels of Gemma’s first birthday, an anniversary underscored by impressive adoption metrics. Gemma models have achieved more than 100 million downloads and spawned the creation of over 60,000 community-built variants. Dubbed the “Gemmaverse,” this ecosystem signals a thriving community aiming to democratise AI.  

“The Gemma family of open models is foundational to our commitment to making useful AI technology accessible,” explained Google.

Gemma 3: Features and capabilities

Gemma 3 models are available in various sizes – 1B, 4B, 12B, and 27B parameters – allowing developers to select a model tailored to their specific hardware and performance requirements. These models promise faster execution, even on modest computational setups, without compromising functionality or accuracy.

Here are some of the standout features of Gemma 3:  

• Single-accelerator performance: Gemma 3 sets a new benchmark for single-accelerator models. In preliminary human preference evaluations on the LMArena leaderboard, Gemma 3 outperformed rivals including Llama-405B, DeepSeek-V3, and o3-mini.

• Multilingual support across 140 languages: Catering to diverse audiences, Gemma 3 comes with pretrained capabilities for over 140 languages. Developers can create applications that connect with users in their native tongues, expanding the global reach of their projects.  

• Sophisticated text and visual analysis: With advanced text, image, and short video reasoning capabilities, developers can implement Gemma 3 to craft interactive and intelligent applications—addressing an array of use cases from content analysis to creative workflows.  

• Expanded context window: Offering a 128k-token context window, Gemma 3 can analyse and synthesise large datasets, making it ideal for applications requiring extended content comprehension.

• Function calling for workflow automation: With function calling support, developers can utilise structured outputs to automate processes and build agentic AI systems effortlessly.

• Quantised models for lightweight efficiency: Gemma 3 introduces official quantised versions, significantly reducing model size while preserving output accuracy—a bonus for developers optimising for mobile or resource-constrained environments.

The model’s performance advantages are clearly illustrated in the Chatbot Arena Elo Score leaderboard. Despite requiring just a single NVIDIA H100 GPU, the flagship 27B version of Gemma 3 ranks among the top chatbots, achieving an Elo score of 1338. Many competitors demand up to 32 GPUs to deliver comparable performance.

One of Gemma 3’s strengths lies in its adaptability within developers’ existing workflows.  

• Diverse tooling compatibility: Gemma 3 supports popular AI libraries and tools, including Hugging Face Transformers, JAX, PyTorch, and Google AI Edge. For optimised deployment, platforms such as Vertex AI or Google Colab are ready to help developers get started with minimal hassle.  
• NVIDIA optimisations: Whether using entry-level GPUs like Jetson Nano or cutting-edge hardware like Blackwell chips, Gemma 3 ensures maximum performance, further simplified through the NVIDIA API Catalog.  
• Broadened hardware support: Beyond NVIDIA, Gemma 3 integrates with AMD GPUs via the ROCm stack and supports CPU execution with Gemma.cpp for added versatility.

For immediate experiments, users can access Gemma 3 models via platforms such as Hugging Face and Kaggle, or take advantage of the Google AI Studio for in-browser deployment.

Advancing responsible AI  

“We believe open models require careful risk assessment, and our approach balances innovation with safety,” explains Google.  

Gemma 3’s team adopted stringent governance policies, applying fine-tuning and robust benchmarking to align the model with ethical guidelines. Given the models enhanced capabilities in STEM fields, it underwent specific evaluations to mitigate risks of misuse, such as generating harmful substances.

Google is pushing for collective efforts within the industry to create proportionate safety frameworks for increasingly powerful models.

To play its part, Google is launching ShieldGemma 2. The 4B image safety checker leverages Gemma 3’s architecture and outputs safety labels across categories such as dangerous content, explicit material, and violence. While offering out-of-the-box solutions, developers can customise the tool to meet tailored safety requirements.

The “Gemmaverse” isn’t just a technical ecosystem, it’s a community-driven movement. Projects such as AI Singapore’s SEA-LION v3, INSAIT’s BgGPT, and Nexa AI’s OmniAudio are testament to the power of collaboration within this ecosystem.  

To bolster academic research, Google has also introduced the Gemma 3 Academic Program. Researchers can apply for $10,000 worth of Google Cloud credits to accelerate their AI-centric projects. Applications open today and remain available for four weeks.  

With its accessibility, capabilities, and widespread compatibility, Gemma 3 makes a strong case for becoming a cornerstone in the AI development community.

(Image credit: Google)

See also: Alibaba Qwen QwQ-32B: Scaled reinforcement learning showcase

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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According to multiple sources familiar with the matter, Apple is in advanced talks to use Alibaba’s Qwen AI models for its iPhone lineup in mainland China. The move would depart from Apple’s global strategy of using OpenAI’s GPT models for its AI features, highlighting the company’s willingness to adapt to local market conditions.

The technical edge of Qwen AI

Qwen AI is attractive to Apple in China because of the former’s proven capabilities in the open-source AI ecosystem. Recent benchmarks from Hugging Face, a leading collaborative machine-learning platform, position Qwen at the forefront of open-source large language models (LLMs).

The platform’s data shows Qwen-powered models dominating the top 10 positions in performance global rankings, demonstrating the technical maturity that Apple seeks for its AI integration.

“The selection of Qwen AI for iPhone integration would validate Alibaba’s AI capabilities,” explains Morningstar’s senior equity analyst Chelsey Lam. “This could be particularly important for Apple’s strategy to re-invigorate iPhone sales in China, where AI features have become increasingly important for smartphone users.”

Regulatory navigation and market impact

The potential partnership reflects an understanding of China’s AI regulatory landscape. While Apple’s global AI features remain unavailable in China due to regulatory requirements, partnering with Alibaba could provide a compliant pathway to introduce advanced AI capabilities.

Market reaction to the news has been notably positive:

• Alibaba’s stock surged 7.6% on Monday, followed by an additional 1.3% gain on Tuesday
• Apple shares responded with a 2.2% increase
• The tech sector has shown renewed interest in China-focused AI integration strategies

Development timeline and expectations

The timing of the potential collaboration aligns with Apple’s upcoming China developer conference in Shanghai, scheduled for March 25. Industry observers speculate the event could serve as a platform on which to announce the integration of Qwen AI features into the iPhone ecosystem.

“The partnership could change how international tech companies approach AI localisation in China,” noted a senior AI researcher at a leading Chinese university, speaking anonymously. “It’s not just about technology integration; it’s about creating a sustainable model for AI development in China’s regulatory framework.”

Implications for developers and users

For Chinese iOS developers, the potential integration of Qwen AI presents opportunity. The partnership could enable:

• Creation of locally optimised AI applications
• Enhanced natural language processing capabilities specific to Chinese users
• Seamless integration with local services and platforms

Prospects and industry impact

The effects of the partnership extend beyond immediate market concerns. As global tech companies navigate operating in China, the Apple-Alibaba collaboration could serve as a blueprint for future integration.

For Alibaba, securing Apple as a flagship partner could catalyse more partnerships with global technology companies seeking AI solutions for China. The collaboration would demonstrate Qwen AI’s capability to meet the stringent requirements of one of the world’s most demanding tech companies.

Looking ahead

While both companies maintain official silence on the partnership, the tech community awaits announcements at the upcoming Shanghai developer conference. The development is important when AI capabilities increasingly influence smartphone purchasing decisions. For Apple, success in China will impact its global growth trajectory, and integrating Qwen AI could provide the competitive edge it needs to maintain its premium market position against local manufacturers offering advanced AI features.

It underscores a broader trend in the tech industry: the growing importance of localised AI solutions in major markets.

See also: Has Huawei outsmarted Apple in the AI race?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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