José Raúl González Alonso

Get a deeper look into the results submitted by NVIDIA in #MLPerf Inference v4.1, highlighted by the first ever submission using the NVIDIA Blackwell architecture, increased performance using the NVIDIA H200 Tensor Core GPU, and more.

1

Stanford Ph.D. researcher Cheng Chi discusses the development of the UMI gripper and the use of diffusion AI models for robotics. The post Stanford researcher discusses UMI gripper and diffusion AI models appeared first on The Robot Report. https://lnkd.in/gbTiRwh4

1

𝟲𝟬 𝗗𝗶𝗿𝗲𝗰𝘁 𝗥𝗲𝗽𝗼𝗿𝘁𝘀, 𝗡𝗼 𝟭:𝟭 𝗠𝗲𝗲𝘁𝗶𝗻𝗴𝘀 Having a #leadership style that encourages #innovation, #inclusivity, and continuous #learning is essential. #JensenHuang, the leader of #Nvidia, is a great example of this approach. Huang avoids the traditional top-down communication. Instead, he maintains a flat organizational structure where he directly connects with up to as many as 60 people. He prefers group discussions over one-on-one meetings, making sure everyone, from new employees to top executives, is on the same level. This not only shares information more widely but also brings in diverse ideas, creating a culture of #transparency and #respect. Continuous learning is a key part of Huang's leadership. In an industry where technology changes quickly, staying up-to-date is crucial. Huang surrounds himself with experts who are willing to share their knowledge, creating an environment rich in learning and growth. At Nvidia, meetings are open to everyone, no matter their rank. This means anyone can join any meeting and access all information. Huang believes that strategic decisions should be made with input from the whole team, not just a few people. This open-door policy breaks down barriers and encourages collaboration. By valuing inclusivity, continuous learning, and open communication, Jensen Huang has built a workplace where innovation can flourish and every voice is heard. His leadership style shows that great leaders empower their teams, encourage learning, and remove obstacles to working together. This not only drives success but also helps everyone reach their full potential. Let's learn from Huang's example. We should create environments where knowledge is shared freely, everyone’s input is valued, and continuous learning is a collective effort. Together, we can build organizations that are not only successful but also nurturing and inclusive, driving innovation and growth from within.

3

The Next War for Talent: AMD vs. NVIDIA Partners For the past couple of years, I’ve been collaborating with AI Cloud and NVIDIA partners. Talent attraction has been a key priority, and we've worked closely with hiring managers to draw in top-tier talent. Recently, however, I've noticed AMD making significant strides, with some of our clients now exclusively betting on AMD. With the landscape in this market changing, will aligning with a single vendor impact partners' ability to attract the best and brightest talent, or will this focused approach drive better results? This raises several potential opportunities and challenges for hiring managers: • How you can best leverage your technology partnerships to attract top talent? • What's your ability to highlight impactful projects with partners to ensure prominent visibility in the market? • Managing potential impact of the diversity of skills and agility within your teams to pivot on technologies • Being aware certain skillsets may become more or less valuable depending on which vendor dominates It looks like there will be interesting times to come on who leads the race to secure the top talent. I’d love to hear your thoughts or for you to share the opportunities/challenges you can potentially see taking place?

20

The 1st deadline for the NVIDIA Academic Grant program is in 1 week. We're accepting proposals for #GenAI #LLMs and Simulation & Modeling. You mustbe a full-time faculty member at an accredited academic institution that awards research degrees to PhD students. NVIDIA will be funding proposals for innovative work related to generative AI and large language models (LLMs), including multi-modal models and content generation, such as diffusion models. Projects should incorporate pretrained models from ai.nvidia.com or use NIMs, Modulus, TensorRT(-LLM), NeMo, NeMo Retriever, NeMo Data Curator, BioNemo, NeMo Guardrails, or Isaac Sim/Lab. Gen AI & LLM Proposals should be attached to one of more of the following themes: Techniques for Training, Customizing, and Accelerating GenAI Exploring New Approaches and Architectures for LLMs and GenAI or Foundational Models and Scientific Applications Using GenAI Simulation Proposals should be attached to one of more of the following themes: Simulation and modeling that incorporate Modulus, Omniverse, HPC SDK, cuNumeric, cuQuantum, or CUDA Quantum. Proposals should be attached to one of more of the following themes: Computational Fluid Dynamics Physics-Informed Machine Learning Quantum Computing Scientific Simulation Selected Principal Investigators (PIs) may receive Up to 32,000 A100 40GB hours or equivalent OR up to 8 RTX 6000 Ada

3

The blog emphasizes accuracy in #AI applications, further optimizations based on customer needs, the role of HEYINTELLIGENCE, and the use of kernel optimization techniques. #TogetherWeAdvance https://lnkd.in/gEwKhbrY

1