Tag: nemo
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NVIDIA NeMo Framework: Training and Fine-Tuning at Scale (NVIDIA AI Series, Part 22)
What the NVIDIA NeMo framework is: Megatron-Core parallelism, NeMo 2.0 Python recipes and NeMo-Run, Megatron Bridge for Hugging Face interop, and when to fine-tune instead of pretrain.
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NVIDIA Nemotron Foundation Models: Open Weights from Nano to Ultra (NVIDIA AI Series, Part 26)
NVIDIA’s Nemotron family explained: genuinely open weights, data and recipes; the hybrid Mamba-Transformer MoE architecture; Nano, Super and Ultra; and when to self-host open models instead of calling a proprietary API.
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Multi-Node LLM Training: Scheduling, Checkpointing and Fault Tolerance (NVIDIA AI Series, Part 25)
At thousands of GPUs, failures are routine. This part covers gang scheduling (Slurm vs Kubernetes vs NVIDIA Run:ai), async distributed checkpointing with NeMo, and the NVIDIA Resiliency Extension stack for fault tolerance, straggler detection, and elastic restart.
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NeMo Customization: LoRA, SFT, and RLHF on NVIDIA NeMo (NVIDIA AI Series, Part 23)
A practical decision guide for AI infrastructure architects on the full NeMo customization spectrum: when to use LoRA, full SFT, DPO, or GRPO, what data and GPU budget each method needs, and how the NeMo Customizer microservice ties it all together.
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Data Preparation at Scale with NeMo Curator (NVIDIA AI Series, Part 24)
NeMo Curator is NVIDIA’s GPU-accelerated data curation toolkit that runs exact dedup, fuzzy dedup, semantic dedup, heuristic filtering, classifier-based quality filters, and PII redaction at trillion-token scale using RAPIDS cuDF and Dask. Learn why investing in data curation beats buying more GPUs.
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What the NVIDIA AI Stack Actually Is, End to End (NVIDIA AI Series, Part 1)
NVIDIA AI is not one product, it is a stack roughly nine layers deep from silicon to agents. Part 1 maps the whole thing: GPUs, CUDA, the operators, TensorRT-LLM, Triton, Dynamo, NIM, NeMo, Nemotron, Blueprints and the AI Enterprise wrapper that supports it all.
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Fine-Tuning Models on VMware Private AI with NeMo Customizer: LoRA, Full SFT and When to Bother (Private AI Series, Part 27)
RAG is not always the answer. Here is how NeMo Customizer fine-tunes models on VMware Private AI, the difference between LoRA and full SFT, and an honest take on when customization beats retrieval.
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What is NVIDIA NeMo — and Why It Matters for Agentic AI
When people talk about AI systems, they often focus on models or APIs. But once you move beyond simple use cases, a bigger challenge appears: How do you control, guide, and manage AI behavior in real-world systems? This is where NVIDIA NeMo becomes critical. If NIM is the layer that runs AI models, then NeMo…
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Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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