28 stories
For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster...
NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two...
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds.
Go ahead and axolotl questions. Contribute to axolotl-ai-cloud/axolotl development by creating an account on GitHub.
The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.
Frontier model training has pushed GPUs and AI systems to their absolute limits, making cost, efficiency, power, performance per TCO, and reliability central to the discussion on effective training. The Hopper vs Blackwell comparisons are not as simple as Nvidia would have you believe.
In retrieval-augmented generation (RAG) applications, the quality of the retrieved documents plays a critical role in delivering accurate and meaningful responses. But what happens when embedding similarity is not enough to get an accurate ordering of the reference documents?
In our previous article, we explored how to build and deploy two key AI pipelines in a retrieval-augmented generation (RAG) application using Haystack with NVIDIA NIM: Indexing pipeline: Prepares data by preprocessing, chunking, and embedding PDF files, finally storing them in a vector database.
Retrieval-augmented generation (RAG) systems combine generative AI with information retrieval for contextualized answer generation. Building reliable and performant RAG applications at scale is challenging.
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