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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.

Elon Musk's xAI has announced the launch of an artificial intelligence training system named 'Colossus', powered by 100,000 Nvidia H100 GPUs, which is considered the most powerful AI training system globally. Its performance may even surpass that of the U.S. Department of Energy's Aurora supercomputer. The power of Colossus is largely attributed to Nvidia's H100 GPU, which operates language models at a speed 30 times faster than previous GPUs, especially in transformer-based tasks.

Elon Musk showcased the internal structure of Tesla's Cortex AI supercomputer cluster being built at the Austin headquarters, which is expected to require 130 megawatts of cooling and power upon launch, growing to 500 megawatts by 2026. The cluster contains 70,000 AI servers, primarily used to solve real-world AI issues, particularly for training Tesla's full self-driving system and the AI for the Optimus robot. Cortex will rely entirely on Nvidia's hardware at launch, while Tesla's own hardware is expected.

Meta has trained its latest AI model, Llama 3.1, using 16,384 GPUs, showcasing the astonishing speed of AI technological advancement. However, during this process, an average of one failure occurs every 3 hours, totaling 419 failures, with approximately half related to the H100 GPUs and their HBM3 memory. This data reveals the reliability challenges faced by supercomputing systems in their pursuit of performance breakthroughs. The complexity of the Llama 3.1 training cluster is comparable to a small city's neural network, with frequent failures. The Meta team has implemented strategies such as reducing...

GPU cloud computing company Lambda has launched a 1-Click Cluster Service, allowing users to access Nvidia H100 GPUs and Quantum 2 InfiniBand clusters on demand, especially suitable for companies needing GPU compute power for short-term projects. This innovative service simplifies the coordination of hardware and software, with no long-term contracts required; users simply reserve when needed, with a minimum reservation time of two weeks. Founded in 2012, Lambda recently raised $320 million, valuing the company at $1.5 billion, indicating strong market demand for its services.

Recently, Musk announced on the social platform X (formerly known as Twitter) that his company, xAI, is conducting training on the world's most powerful AI training cluster — the "Memphis Supercluster" located in Memphis, Tennessee. This cluster will be the largest capital investment in the city's history, attracting significant media attention. According to local news reports, this supercluster is equipped with 100,000 liquid-cooled Nvidia H100 graphics processing units (GPUs). These chips, in

On July 9th, Musk announced that his artificial intelligence company xAI is building a supercomputer with 100,000 NVIDIA H100 GPUs, expected to be delivered and begin training by the end of this month. This move marks the termination of xAI's negotiations with Oracle to expand the existing agreement and rent more NVIDIA chips. Musk emphasized that this will become "the most powerful training cluster globally with a significant lead." He said that the core competitive edge of xAI lies in speed, "

Elon Musk recently announced that his artificial intelligence startup, xAI, is set to launch its revolutionary large language model, Grok-2, in August, signaling the arrival of more advanced AI capabilities. Although Grok-2 has not yet been fully unveiled, Musk is already excitedly hyping up the upcoming masterpiece, Grok-3. When discussing AI development, Musk particularly emphasized the importance of datasets and the arduous task of cleaning data for large language models (LMMs). He also did n

Meta has released two 24K H100 GPU clusters for training Llama-3. Llama-3 utilizes RoCEv2 networking and Tectonic/Hammerspace's NFS/FUSE network storage. It is expected that Llama-3 will go live by the end of April or mid-May, possibly as a multimodal model and will continue to be open-sourced. Meta plans to have 600,000 H100 computing power by the end of 2024.

The delivery time for NVIDIA H100 GPU has been shortened to 3-4 months, and some companies have begun reselling excess H100 80GB processors. Demand for AI chips still exceeds supply, and cloud service providers offer on-demand rental services for H100 GPUs. Prices for NVIDIA H100 and similar processors have not decreased, and companies are placing more emphasis on pricing and procurement rationality. The AI sector may witness a more balanced market landscape.