GPU

AMD mass-produces AI chip with higher inference ability than GPU by the tip of this 12 months… Stock price falls

AMD released a brand new artificial intelligence (AI) chip and server chip, difficult Nvidia and Intel, the leaders in each market. Nonetheless, the market's response appears to be somewhat cold. Reuters and CNBC reported on...

NVIDIA, Blackwell GPU sold out for the following 12 months… “Market share expected to rise further next yr”

It was revealed that each one quantities of NVIDIA's latest artificial intelligence (AI) chip 'Blackwell' to be produced over the following 12 months have been reserved. Through this, the market share is predicted to...

“That is the world’s best AI server”… MS unveils NVIDIA’s ‘Blackwell’ server price 2.7 billion for the primary time

Microsoft (MS) introduced the world's first server composed of NVIDIA's latest 'Blackwell' chip. Contrary to expectations that the server can be accepted in early December, it was revealed that the server was already in...

SKT Gasan Data Center Expands to GPU-Dedicated AI Center

SKT is opening a man-made intelligence (AI) data center in Seoul in partnership with cloud startup Lambda. SK Telecom (CEO Yoo Young-sang) announced on the twenty first that it has signed a partnership with Lambda...

Meta: “Rama 4 Training Uses 10x More GPUs Than Rama 3.1”

Meta announced that it is going to train its next-generation model, Rama 4, with 10 times more GPUs than Rama 3.1. Which means that it is going to construct a cluster of roughly 160,000...

GIST, NVIDIA Conduct Multi-Node GPU Programming Training

Gwangju Institute of Science and Technology (GIST, President Lim Ki-cheol) announced on the twenty sixth that it held a deep learning model training (DLI Day) along with the Supercomputing Center (Director Kim Jong-won) and...

Optimizing LLM Deployment: vLLM PagedAttention and the Way forward for Efficient AI Serving

Large Language Models (LLMs) deploying on real-world applications presents unique challenges, particularly when it comes to computational resources, latency, and cost-effectiveness. On this comprehensive guide, we'll explore the landscape of LLM serving, with a...

Flash Attention: Revolutionizing Transformer Efficiency

As transformer models grow in size and complexity, they face significant challenges by way of computational efficiency and memory usage, particularly when coping with long sequences. Flash Attention is a optimization technique that guarantees...

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