Travis Britton’s Post

Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning https://lnkd.in/eX8k9whM

#HPC and #AI #deeplearningalgorithms are neural networks are based on #MATRIXMath where AVX512 and Intel's new AMX accelerate these workflows helping clients put the right workloads on the right platform at the right point in time.

NEED FOR SPEED - The upcoming OCI BM.GPU.GH200 instance is a bare-metal GPU shape designed to support the most intense AI inference workloads that need fast, high-capacity memory for large language models (LLMs) and recommender systems. Other workloads that can use these instances include vector databases, scientific and high-performance computing, graph neural networks (GNNs) and single-instance LLM inference.

As we are hearing every day about Generative AI, GPUs are the power that enables the deep learning required. Not having a deep understanding of GPUs, this was a great deep dive into that processing power. https://buff.ly/404If6p #gpu #deeplearning #generativeai

To GPU or Not to GPU? The age of Generative AI is powered by three components: 1) Availability of huge datasets, 2) Breakthrough in algorithm such as transformer, etc, and 3) Availability of computer power, mainly through GPU. Now it is possible to train, fine tune and infer using consumer grade GPU, but current rush to acquire GPU either by large giants such as OpenAI, Meta, Google and so on, plus the consumer sections push the price up and limit the supply of these GPUs. The InfoWorld article discusses whether GPUs are essential for generative AI systems. It acknowledges the strengths of GPUs in AI tasks due to their parallel processing capabilities but points out that alternatives like CPUs, TPUs, FPGAs, and APUs might be more cost-effective for certain applications. The article emphasizes the importance of considering the specific requirements of a project before deciding on the hardware, suggesting that advancements in AI algorithms could make non-GPU processors viable options. For a detailed understanding, you can read the article: https://lnkd.in/gr-Ri55B

🚨 NVIDIA H100 GPUs are now available on Paperspace! Leverage the simplicity, reliability, and predictable pricing of Paperspace to run complex AI/ML models and deliver powerful #AI experiences. Do reach out to if you are looking for an Easy and Affordable way to Train, Quantize, Finetune and Deploy LLMs or other Deep Learning Models at scale. Joshua Robison Kanishka Roychoudhury, CFA Learn more 🔗 https://do.co/3S2faos

We see greatly increased demand and interest in HPC, GenAI, deep learning and ML space. We’re literally looking at investments of tens of millions of € in next few years in the SEE region. Many customers are talking about it, and some already started experimenting (according to the old “teenage sex” adage). We’re getting questions not only about potential use cases, but also platforms. Below article describes HPE’s highly anticipated GenAI supercomputer platform - designed to help companies create, fine-tune and run large language models in their own data centers. Basically, a true GenAI solution that goes beyond just hardware - and includes all of the required software and services that developers need to build advanced models. Speaking of hardware - yes, new Blackwell GPUs, which were just announced at GTC 2024, will be supported.

Unleash AI Power 🦾 Discover how to choose the right GPU for your AI computing needs in our latest article. 💻

Leveraging the NVIDIA CUDA compute platform, CUDA-X libraries will be able to expedite data processing across diverse data types #AI #GenAI https://lnkd.in/gCjCHJjd