Abstract This paper challenges the well-established paradigm for building any-to-any networks for training Large Language Models (LLMs). We show that LLMs exhibit a unique communication pattern where only small groups of GPUs require high-bandwidth any-to-any communication within them, to achieve near-optimal training performance. Across these groups of GPUs, the communication is insignificant, sp
Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly. This historical treatment is odd given that hardware and software have frequently determined which research ideas succeed (and fail). This essay introduces the term hardware lottery to describe when a research idea wins because
About five years ago, artificial intelligence research organization OpenAI pitched Microsoft on a bold idea that it could build AI systems that would forever change how people interact with computers. At the time, nobody knew it would mean AI systems that create pictures of whatever people describe in plain language or a chatbot to write rap lyrics, draft emails and plan entire menus based on a ha
thread#showTweet" data-screenname="tim_zaman" data-tweet="1636981863477809152" dir="auto"> Azure/Microsoft released pics of OpenAI's next-gen AI datacenter. This supposedly powers [Chat]GPT* training (inference?). From the pics we can infer most components, power, network/ib topology, layouts, vendors and more. A thread 🧵[1/7] thread#showTweet" data-screenname="tim_zaman" data-tweet="163698186444
What is RAPIDSRAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries. Built on state-of-the-art foundations like NVIDIA CUDA and Apache Arrow, it unlocks the speed of GPUs with code you already know. Learn more on the About Section Why Use RAPIDSRAPIDS allows fluid, creative interaction with data for everyone from BI users to AI researchers on the cutting e
一週間ほど前から,NVIDIAがGeForceのデータセンタ利用を制限した話がネットを賑わしはじめた.おもしろいことになってるなと感じたので,私の経験と,それに照らし合わせて今回の騒動に感じた事を語りたくなった.記憶も記録も曖昧であるため,年寄りの昔話程度に読んでもらいたい. ここでいうおもしろいとは,滑稽という意味ではなく,興味深いという意味合いである.この件で色々な対応を迫られている方々を蔑む意図は微塵もないことはご理解いただきたい. 今回の騒動に抱いた感想俗っぽい言い方をすると「恋人がこちらをフって乗り換えた相手が意外に曲者で,元恋人が困っているのを眺める気持ち」だろうか.そのような経験をしたことはないし,恋人というほどNVIDIA社に固執しているわけでもないが. 事の発端NVIDIAが規約変更によりGeForceのデータセンター利用を制限。大学などの研究活動にも大ブレーキ これが最
PlayerUnknown's Battlegrounds (PUBG) has taken the gaming community by surprise by becoming one of the best-selling releases of 2017, with over 8 million players in only five months. Recently, PUBG made history when it broke 699,000 concurrent players on Steam, a feat only achieved by two other games. Every match in PlayerUnknown’s Battlegrounds is guaranteed to provide amazing in-game moments, cl
Benchmarking TensorFlow on Cloud CPUs: Cheaper Deep Learning than Cloud GPUs I’ve been working on a few personal deep learning projects with Keras and TensorFlow. However, training models for deep learning with cloud services such as Amazon EC2 and Google Compute Engine isn’t free, and as someone who is currently unemployed, I have to keep an eye on extraneous spending and be as cost-efficient as
Capacity droughts hit just before conference paper deadlines, say researchers Top cloud providers struggled to provide enough GPUs on-demand last week, AI experts complained to The Register. As a deadline for research papers loomed for a major conference in the machine-learning world, teams around the globe scrambled to rent cloud-hosted accelerators to run tests and complete their work in time to
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