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AMD's advancements in AI are noteworthy, particularly its latest optimizations for the Stable Diffusion model. Recently, Stability AI released an ONNX-optimized version of Stable Diffusion, resulting in significantly improved performance for AMD Radeon GPUs and Ryzen integrated graphics in AI tasks, with speed increases up to 3.8 times faster. This progress narrows the gap with NVIDIA's ecosystem...

Former Intel CEO Pat Gelsinger recently criticized Nvidia's pricing strategy for its AI GPUs on the Acquired podcast during Nvidia's 2025 GPU Technology Conference, arguing that the high cost makes them unsuitable for large-scale AI inference tasks. Gelsinger highlighted that inference is crucial for deploying AI models and that the industry should focus more on inference, a domain where Nvidia's technology falls short in terms of cost-effectiveness. Image source omitted.

Meituan CEO Wang Xing detailed the company's strategic plan for artificial intelligence (AI). Wang revealed that over the past year, Meituan prioritized securing GPU resources, investing heavily in AI infrastructure. He further stated that Meituan plans to further increase investment in key AI infrastructure in 2025 to strengthen its position in this field.

HPC-AI Tech recently announced Open-Sora 2.0, a groundbreaking video AI system achieving commercial-grade quality at approximately one-tenth the typical training cost. This represents a potential paradigm shift in the resource-intensive field of video AI, mirroring efficiency gains seen in language models. While existing high-quality video generation systems like Movie Gen and Step-Video-T2V can cost millions of dollars to train, Open-Sora 2.0 offers a significantly more affordable alternative.

In the midst of the AI revolution, no company is better positioned than Nvidia. Since ChatGPT ignited the market over two years ago, the high-performance GPU giant's revenue, profitability, and cash reserves have skyrocketed, driving its stock price higher. Fueled by its growing wealth, Nvidia is aggressively expanding its investments in AI, particularly in startups. According to PitchBook data, Nvidia significantly increased its venture capital activity in 2024, participating in 49 rounds of AI-related funding.

Google CEO Sundar Pichai announced at a launch event that Google has open-sourced its latest multimodal large model, Gemma-3. This model is attracting significant attention for its low cost and high performance. Gemma-3 offers four different parameter scale options: 1 billion, 4 billion, 12 billion, and 27 billion parameters. Surprisingly, the largest 27 billion parameter model only requires a single H100 GPU for efficient inference, while similar models often require ten times the computing power.

Social media giant Meta is facing unprecedented AI infrastructure costs, with projected AI-related spending soaring to a staggering $65 billion, potentially contributing to a total annual expenditure of $119 billion! Faced with this astronomical bill, the tech giant has decided to develop its own AI chips, and has already made significant progress. Recent reports indicate that Meta is about to begin a small-scale deployment of its custom chips, signaling a gradual move away from Nvidia and its reliance on their GPUs.

GPU cloud services company CoreWeave recently announced a five-year, $119 billion strategic partnership agreement with OpenAI. Under the agreement, CoreWeave will provide OpenAI with the necessary computing power to support the training and delivery of its AI models. This significant deal marks a deepening collaboration between the two companies in the field of artificial intelligence. As part of the collaboration, CoreWeave will also issue $3...

Nvidia today announced that its premier global AI conference, GTC 2025, will be held in San Jose, California from March 17th to 21st. The event is expected to draw 25,000 in-person attendees and 300,000 online viewers, showcasing the latest breakthroughs in AI and accelerated computing. A highly anticipated keynote address from Nvidia CEO Jensen Huang will highlight the conference.

Alibaba has open-sourced its new inference model, Tongyi Qianwen QwQ-32B, enabling users with consumer-grade GPUs to achieve surprisingly high performance. This makes advanced AI capabilities accessible to a wider audience.

The DeepBeepMeep team recently released Wan2.1GP on GitHub, a video generation model optimized for users with low-end GPUs. Based on Alibaba's Wan2.1, this model aims to provide powerful video generation capabilities to those lacking access to high-performance GPU resources. The launch of Wan2.1GP marks a significant advancement in video generation technology, particularly within the open-source community. Image source note: Image generated by AI, licensed from Midjourney.