CoreWeave, The GPU Champion That Isn't NVIDIA
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Go Subscribe!📝 Editorial: CoreWeave, The GPU Champion That Isn't NVIDIAIf you've never heard about CoreWeave and you're in the AI world, believe me, you soon will. The GPU cloud platform has become synonymous with high-scale computing in the AI industry. When we talk about AI hardware, NVIDIA immediately comes to mind. How could it not? The chip giant has experienced a surge in revenue and earnings thanks to the rapid rise of AI technologies. However, NVIDIA is not the only company benefiting from the GPU craze; CoreWeave has emerged from obscurity, raising more than $400 million in equity and almost $2.3 billion in debt, making it one of the most important compute offerings of this AI generation. CoreWeave's origins are not in the AI space; the company began in 2017 as a miner for the Ethereum blockchain. However, when AI started gaining traction, CoreWeave swiftly pivoted to building a GPU-accelerated cloud optimized for NVIDIA hardware. The CoreWeave platform includes native Kubernetes orchestration, support for the top GPU accelerators, as well as storage and networking capabilities. While CoreWeave remains highly reliant on NVIDIA GPUs today, its offerings are expanding rapidly. The success of CoreWeave has materialized in approximately $500 million in revenue forecasted for this year and back-to-back $200M+ equity rounds in April-May. Just last week, CoreWeave announced securing a remarkable $2.3 billion in debt financing, led by Magnetar Capital and Blackstone. The proceeds of this financing will be used to purchase more NVIDIA GPUs, build 12 new data centers, and, obviously, attract talent. CoreWeave already has many of the marquee AI platforms in the market on board. CoreWeave is becoming a common component of large-scale infrastructure for foundation models. They've expertly ridden the GPU wave. 🔎 ML ResearchLLM AttacksResearchers from Carnegie Mellon University published a paper that proposes an attack method for LLMs. Specifically, the technique uses a suffix across a large number of queries that causes the LLMs to produce more affirmative responses —> Read more. LoraHubResearhcers from the University of Washing and Allen Institute for AI published a paper detailing LoraHub, a framework that takes advantage of the Low-Rank Adaptation (LoRA) technique for tasks other than basic fine-tuning. LoraHub combines different LoRA modules to adapt models to unseen tasks —> Read more. 🤖 Cool AI Tech ReleasesAudioCraftMeta AI released AudioCraft, a family of generative AI models for text-to-audio generation —> Read more. ONNX ScriptMicrosoft open sourced ONNX Script, a library that enables writing ONNX ML pipelines directly in Python —> Read More. llama2.cAdrej Karpathy released llama2.c that generates a lighweight, faster , 500 lines of C code based on the binaries of a trained version of Llama 2 —> Read more. 🛠 Real World MLEmbeddings at UberThe Uber engineering team discusses the use of two-tower embedding models for improving recommendations —> Read more. 📡AI Radar
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