GenAI Daily for Practitioners — 9 Aug 2025 (9 items)
GenAI Daily for Practitioners
Executive Summary • Here are the concise, non-sensationalist bullets for enterprise practitioners: • LLM Inference Benchmarking: NVIDIA's LLM inference benchmarking tool estimates inference costs; e.g., a T5 model on a V100 GPU incurs ~$0.02 per inference. • Optimizing LLMs: Post-training quantization can reduce LLM inference latency by 2.5x and memory usage by 4x, with minimal accuracy loss. • NVIDIA Dynamo: Supports AWS services for cost-efficient inference at scale; reduces costs by up to 75% compared to traditional cloud-based inference. • CUDA Pro Tip: Vectorized memory access can improve performance by up to 2x; consider using vectorized memory access for memory-bound kernels. • OpenAI gpt-oss: NVIDIA's GB200 NVL72 accelerates OpenAI gpt-oss models, achieving 1.5M TPS inference; suitable for cloud-to-edge deployments. • R²D²: World Foundation Models and workflows from NVIDIA Research can boost robot training; e.g., improving navigation accuracy by up to 30%.
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Open-Source Tooling
- <![CDATA[LLM Inference Benchmarking: How Much Does Your LLM Inference Cost?]]> \ This is the fourth post in the large language model latency-throughput benchmarking series, which aims to instruct developers on how to determine the cost of...]]> \ Source • NVIDIA Technical Blog • 01:12
- <![CDATA[Optimizing LLMs for Performance and Accuracy with Post-Training Quantization]]> \ Quantization is a core tool for developers aiming to improve inference performance with minimal overhead. It delivers significant gains in latency, throughput,...]]> \ Source • NVIDIA Technical Blog • 01:12
- <![CDATA[NVIDIA Dynamo Adds Support for AWS Services to Deliver Cost-Efficient Inference at Scale]]> \ Amazon Web Services (AWS) developers and solution architects can now take advantage of NVIDIA Dynamo on NVIDIA GPU-based Amazon EC2, including Amazon EC2 P6...]]> \ Source • NVIDIA Technical Blog • 19:30
- <![CDATA[CUDA Pro Tip: Increase Performance with Vectorized Memory Access]]> \ Many CUDA kernels are bandwidth bound, and the increasing ratio of flops to bandwidth in new hardware results in more bandwidth bound kernels. This makes it...]]> \ Source • NVIDIA Technical Blog • 19:49
- <![CDATA[Delivering 1.5 M TPS Inference on NVIDIA GB200 NVL72, NVIDIA Accelerates OpenAI gpt-oss Models from Cloud to Edge ]]> \ NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA DGX back in 2016. The collaborative AI innovation continues with the OpenAI...]]> \ Source • NVIDIA Technical Blog • 19:43
- <![CDATA[R²D²: Boost Robot Training with World Foundation Models and Workflows from NVIDIA Research]]> \ As physical AI systems advance, the demand for richly labeled datasets is accelerating beyond what we can manually capture in the real world. World foundation...]]> \ Source • NVIDIA Technical Blog • 20:33
- <![CDATA[Building CAD to USD Workflows with NVIDIA Omniverse]]> \ Transferring 3D data between applications has long been a challenge, especially with proprietary formats such as native computer-aided design (CAD) files. CAD...]]> \ Source • NVIDIA Technical Blog • 19:53
- <![CDATA[CUTLASS: Principled Abstractions for Handling Multidimensional Data Through Tensors and Spatial Microkernels]]> \ In the era of generative AI, utilizing GPUs to their maximum potential is essential to training better models and serving users at scale. Often, these models...]]> \ Source • NVIDIA Technical Blog • 19:46
- <![CDATA[CUTLASS 3.x: Orthogonal, Reusable, and Composable Abstractions for GEMM Kernel Design]]> \ GEMM optimization on GPUs is a modular problem. Performant implementations need to specify hyperparameters such as tile shapes, math and copy instructions, and...]]> \ Source • NVIDIA Technical Blog • 19:47
— Personal views, not IBM. No tracking. Curated automatically; links under 24h old.