XPeng and Banma Implement 30B Class AI Model on XPeng's Turing Onboard Chip
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XPeng and Banma Implement 30B Class AI Model on XPeng's Turing Onboard Chip

XPeng, in collaboration with Banma Intelligence, which is supported by Alibaba and SAIC Motor, has developed an automotive solution for an intelligent cockpit capable of running a large 30-billion parameter AI model directly within the vehicle.

The system was unveiled at the Alibaba Cloud Apsara Conference in 2026. It combines XPeng's proprietary Turing chip, which boasts an efficient computing power of 750 TOPS per chip, with Banma's multimodal onboard model, AutoOmni.

The design concept differs from the traditional separation of functions between the car and the cloud. The partners refer to this as an edge-oriented architecture with cloud support. Core functions requiring high frequency are executed locally: continuous multi-turn dialogue, multimodal perception and interaction, answering questions from a local knowledge base, and complex task planning. The cloud is only used for secondary, low-frequency functions such as news and weather.

This approach allows the cockpit to function with minimal latency in tunnels, underground garages, and other areas with poor or no signal, and it also addresses network coverage issues abroad or privacy requirements, according to the partners' statement.

This represents a significant leap in scale: according to a Xinhua report from the Banma forum at the conference, most existing cockpit onboard models have only 4 or 7 billion parameters. Michael, head of XPeng's intelligent computing center, noted that competition in the large model field is shifting from counting parameters in the cloud to inference on the device, and this solution demonstrates stable operation of a 30B class model on an automotive chip.

The Turing chip is designed to serve three of XPeng's product lines simultaneously: AI vehicles, AI robots, and flying cars. Banma provides the operating system and the model layer. The company claims that over 60% of automakers who have integrated onboard cockpit models into production use its solutions. At the same conference, Banma introduced AutoOmni 2.0-23B-A3B, a mixture-of-experts model with approximately 3 billion active parameters. Banma stated that this model handles routine cockpit tasks as well as cloud models ten times larger.

Together, the companies offer automakers a comprehensive solution that includes one chip, an AI operating system, and a large model, instead of separate components. Hao Fei, co-founder of Banma, reported that partners will start with smart cars and then expand joint hardware and software AI solutions to other embodied intelligent devices. They plan to continue adapting the model and improving tooling, focusing on mid-to-high-end vehicles. Neither company named a specific production model or launch date.

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XFEON Company Unveils Physical AI Matrix Featuring Xinghe S1 Chip, AGLobe Brain, and Xinghui Token Workstations
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XFEON Company Unveils Physical AI Matrix Featuring Xinghe S1 Chip, AGLobe Brain, and Xinghui Token Workstations

XFEON, a company based in Chengdu, unveiled a product matrix integrating chips and algorithms at an event held on September 22, 2026. This matrix encompasses edge silicon, embodied data tools, Token workstations, and an embodied brain, according to reports from EEFocus and other local sources referencing www.xfeon.com.

The company's corporate materials use the official English designation XFEON; the presented product lines include Xinghe S1, AGLobe, and Xinghui Token workstations. While Chinese press uses additional explanations, some English funding reports mention Xingfan Intelligence, but this review prefers to use the official XFEON designation.

At the silicon level, Xinghe S1 is positioned as the core computation engine for physical AI at the edge. It is a specialized multi-core design with a proprietary AI acceleration core, supporting native mixed precision INT2–INT8, delivering approximately 105 TOPS equivalent to INT8 on the die, typical power consumption of around 25W, and a claimed efficiency exceeding 4 TOPS/W. Company materials also assert that through co-design of software and hardware, approximately 50% higher inference throughput, about 85% smaller model memory footprint, and roughly 90% less memory usage for continuous learning are achieved compared to previous baseline models.

Around S1, XFEON describes SKU series such as Xinghe R (for robotics, up to 420 TOPS), Xinghe K (for orbital satellite computing), and Xinghe E (for edge/industrial applications).

Regarding the algorithmic component, AGLobe is presented as an embodied brain built around conceptual learning rather than exclusively on vision-language-action pathways or world models. It aims to extract transferable concepts from multiple or even single demonstrations across time, space, quantity, and causality. Computing products include the Xinghui D/N series Token workstations, designed for private deployment, training, and high-performance inference, featuring mixed-precision packages from BF16 to INT2. It is claimed that an eight-card node can host a model with 671 billion parameters.

Supporting components mentioned in reviews include the XGAIA data toolkit, DexCore simulation devices, and the XBoost acceleration layer for the edge. The XFEON matrix, presented in September, represents a vertical stack of physical AI, extending from the Xinghe S1 edge silicon through AGLobe and Xinghui Token systems, and is not merely a chip release or a stock market story.

Shanghai AI Lab releases scientific multimodal model Intern-S2-397B with memory decoder
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Shanghai AI Lab releases scientific multimodal model Intern-S2-397B with memory decoder

Shanghai AI Laboratory has introduced its open-weights, science-oriented multimodal foundation model, Intern-S2-397B, on the Hugging Face and ModelScope platforms. The announcement followed a presentation at the Putian Innovation Forum on September 13, 2026, and a laboratory briefing on September 21.

The official English name of the laboratory is Shanghai AI Laboratory / Intern-S2. This release differs from recent lab publications, such as Atria Dawn, Intern Physical World Model W0, and NCP-ArchPreview, as it represents the release of weights for a scientific model architecture, rather than an announcement of another agent or world model.

The Intern-S2 model is designed to perform long-horizon scientific tasks and agent cycles. Lab materials emphasize the inclusion of a pluggable Memory Decoder, which allows domain modules to be attached without retraining the entire base model. For instance, using Intern-MemDec-4B in biological experiments increased average scores on Biological Instructions from approximately 56.92 to 60.32 while maintaining overall results at the level of the base model.

Hugging Face indicates that the Intern-S2-397B card contains about 403 billion parameters. The model underwent visual pre-training on pages of raw scientific literature, multi-domain scientific reinforcement learning across more than 20 fields, and long-horizon agent reinforcement learning in isolated environments. Support for deployment is provided through LMDeploy, vLLM, and SGLang, and the official Intern API endpoint is documented.

Shanghai AI Laboratory reports that Intern-S2 is deeply optimized in conjunction with Huawei's Ascend computing stack regarding computation, communication, and memory aspects. Furthermore, the model will be integrated into the lab's Intern DuanYan scientific discovery platform, which covers life sciences, materials, semiconductors, and related disciplines. According to vendor statements, Intern-S2 ranks first among open-source models in knowledge, code, and agent sets, demonstrating strong performance in biology and materials science tasks; however, these figures are currently internal lab data pending independent testing.

For research groups, the end result is access to the downloadable Intern-S2-397B weights, accompanied by documentation on the Memory Decoder and a note on collaboration with Ascend. Thus, this is an open multimodal foundation model updated for community use, not merely an announcement based on a closed API.

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