… AI Developer Learn how to help AI developers design, build, and deploy intelligent systems GPU Kernel Developer Learn how to leverage advanced tools and libraries to simplify GPU kernel development Enterprise AI and Infrastructure Learn how to deploy, scale, and manage AI workloads across enterpris… …
… On the Llama 2 70B LoRA fine-tuning benchmark, eight Blackwell GPUs running as part of an NVIDIA DGX GB200 NVL72 system delivered 2.5x more performance compared with a previous submission using an NVIDIA DGX H100 system with eight NVIDIA H100 GPUs. …
Recognizing breakthrough university research advancing the future of technology through deep academic collaboration. …
… CUDA is NVIDIA's platform for accelerated computing, providing the software layer that enables applications to harness the power of GPUs. Developers can program in languages such as C++, Python, and Fortran or leverage GPU-accelerated libraries and frameworks like PyTorch. …
… Likewise, I store my bills, academic records, invoices, product warranties, and other essential documents in my Paperless-ngx server, with Paperless AI letting me leverage my LLMs and the embedding model for RAG-based chats. …
AMD ROCm™ Software Open software stack that includes programming models, tools, compilers, libraries, and runtimes for AI and HPC solution development on AMD GPUs What’s New in ROCm 7 Access Latest Applications Download Solutions Brief Optimized GPU Software Stack AMD ROCm™ is an open software stac… …
… Reduce-scatter gradient : A reduce-scatter is performed over all gradients and each GPU gets a portion of gradients corresponding to the parameters it “owns.” Local updates : Each GPU updates only the specific portion of the model parameters it “owns.” AllGather parameters : After the update, GPUs … …
… The 3x scaling in GPU numbers delivered a 2.8x scaling in performance, a 93% efficiency rate thanks in part to software optimizations. Efficient scaling is a key requirement in generative AI because LLMs are growing by an order of magnitude every year . …
… LLMs are rapidly defeating new benchmarks The capabilities of AI models have improved with incredible speed over the past decade, and as the graph above shows, progress seems to be accelerating. Multimodal LLMs, in particular, are conquering benchmarks nearly as quickly as they can be invented. …
… Initially, they were most interested in coding agents as a way to speed up AI research—automating the grunt work of managing training runs and monitoring GPU clusters. …