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.

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.

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

OpenAI's Sora generates 5 minutes of video per hour on the NVIDIA H100, allowing for 120 minutes of video creation daily to support the TikTok and YouTube creator community, requiring about 720,000 NVIDIA H100 GPUs. Sora will target visual artists and filmmakers after its launch, with plans to incorporate sound and editing flexibility. OpenAI is promoting Sora in Hollywood, allowing creators to generate multiple candidates before uploading videos.

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.

Nvidia's latest quarterly revenue has seen significant growth, with data center products being the main contributor. In the past quarter, Nvidia sold 500,000 H100 GPUs. Meta and Microsoft are the largest customers of the H100, each purchasing 150,000 GPUs. As companies gradually shift to their own GPUs, sales are expected to decline. Analysts believe Nvidia will reach sales targets for the H100 and A100 again in the fourth quarter.

Microsoft has launched the new ND H100v5 virtual machine series, equipped with 8 NVIDIA H100 chips. The virtual machines feature AI supercomputing GPUs, next-generation CPUs, and low-latency networking. When using the new 8-bit FP8 floating-point data type for matrix multiplication operations, the speed has increased sixfold. ND H100v5 VM helps large language models achieve up to two times the inference speed improvement.