# Training AI Models

Published articles for Training AI Models.

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## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine](<https://devfeed.tech/articles/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine-21079.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/accelerating-dropless-moe-training-in-jax-with-nvidia-transformer-engine/>)

Author: Tanya Lenz

Published: 2026-09-14T16:39:15Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [GB200](<https://devfeed.tech/topics/gb200.md>), [Network](<https://devfeed.tech/topics/network.md>), [Python](<https://devfeed.tech/topics/python.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>), [mixtral](<https://devfeed.tech/topics/mixtral.md>), [qwen](<https://devfeed.tech/topics/qwen.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [communication](<https://devfeed.tech/tags/communication.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [mixtral](<https://devfeed.tech/tags/mixtral.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [moe](<https://devfeed.tech/tags/moe.md>), [networks](<https://devfeed.tech/tags/networks.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [router](<https://devfeed.tech/tags/router.md>), [routing](<https://devfeed.tech/tags/routing.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement.

### Source excerpt

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE...

## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference](<https://devfeed.tech/articles/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference-6781.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/co-designing-ai-models-using-speculative-decoding-for-faster-llm-inference/>)

Author: Tanya Lenz

Published: 2026-09-02T16:04:19Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [batch](<https://devfeed.tech/tags/batch.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula.

### Source excerpt

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and...

## How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin

DevFeed: [How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin-6843.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-unlocks-ultrafast-interactivity-at-long-context-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

Published: 2026-08-24T15:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [d-matrix](<https://devfeed.tech/topics/d-matrix.md>), [Vera Rubin](<https://devfeed.tech/topics/vera-rubin.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [gemma4](<https://devfeed.tech/topics/gemma4.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Cache](<https://devfeed.tech/topics/cache.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cache](<https://devfeed.tech/tags/cache.md>), [collective](<https://devfeed.tech/tags/collective.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [low-latency-inference](<https://devfeed.tech/tags/low-latency-inference.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rubin-gpu](<https://devfeed.tech/tags/rubin-gpu.md>), [speed](<https://devfeed.tech/tags/speed.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

NVIDIA Groq 3 LPX, paired with Vera Rubin NVL72, delivers high-interactivity AI inference for long-context workloads. A reported benchmark measured 3,431 output tokens per second on Gemma 4 31B with a 100K context.

### Source excerpt

NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the...

## Building Federated Multimodal AI Workflows with NVIDIA FLARE

DevFeed: [Building Federated Multimodal AI Workflows with NVIDIA FLARE](<https://devfeed.tech/articles/building-federated-multimodal-ai-workflows-with-nvidia-flare-6776.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/building-federated-multimodal-ai-workflows-with-nvidia-flare/>)

Author: Tanya Lenz

Published: 2026-08-19T17:50:47Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [featured](<https://devfeed.tech/tags/featured.md>), [federated-learning](<https://devfeed.tech/tags/federated-learning.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [multimodal-ai](<https://devfeed.tech/tags/multimodal-ai.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [python](<https://devfeed.tech/tags/python.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [updates](<https://devfeed.tech/tags/updates.md>), [vlms](<https://devfeed.tech/tags/vlms.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

The article explains how NVIDIA FLARE supports federated training for multimodal and vision-language models when data remains distributed across sites. It focuses on deciding which model state to exchange and on efficiently transferring and aggregating large updates through externalization, tensor streaming, and disk-backed aggregation.

### Source excerpt

Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data...

## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer

DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer](<https://devfeed.tech/articles/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer-6811.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-optimizer/>)

Author: Tanya Lenz

Published: 2026-08-17T18:12:48Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Nemotron](<https://devfeed.tech/topics/nemotron.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Mamba](<https://devfeed.tech/topics/mamba.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compute](<https://devfeed.tech/tags/compute.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [edge-computing](<https://devfeed.tech/tags/edge-computing.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model](<https://devfeed.tech/tags/model.md>), [model-optimizer](<https://devfeed.tech/tags/model-optimizer.md>), [models](<https://devfeed.tech/tags/models.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [speed](<https://devfeed.tech/tags/speed.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput.

### Source excerpt

Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find...

## NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning

DevFeed: [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning](<https://devfeed.tech/articles/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning-6895.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with-enhanced-in-context-learning/>)

Author: Tanya Lenz

Published: 2026-07-27T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Ising](<https://devfeed.tech/topics/ising.md>), [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [NVFP4](<https://devfeed.tech/topics/nvfp4.md>), [DGX Spark](<https://devfeed.tech/topics/dgx-spark.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [dgx-spark](<https://devfeed.tech/tags/dgx-spark.md>), [featured](<https://devfeed.tech/tags/featured.md>), [ising](<https://devfeed.tech/tags/ising.md>), [nvfp4](<https://devfeed.tech/tags/nvfp4.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

NVIDIA Ising Calibration 1.5 is an open-source vision-language model for interpreting quantum-processor diagnostics and recommending calibration actions. The article highlights zero-shot and in-context learning evaluation on QCalEval, plus an NVFP4-quantized version for local deployment.

### Source excerpt

NVIDIA Ising Calibration is an open source vision language model (VLM) designed to interpret diagnostic outputs from quantum processors and determine how they...

## Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72

DevFeed: [Setting a World Record for MoE Pre-Training on NVIDIA GB300 NVL72](<https://devfeed.tech/articles/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72-6939.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/setting-a-world-record-for-moe-pre-training-on-nvidia-gb300-nvl72/>)

Author: Kirthi Devleker

Published: 2026-07-21T18:30:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [NCCL](<https://devfeed.tech/topics/nccl.md>), [networking](<https://devfeed.tech/topics/networking.md>), [deepseek](<https://devfeed.tech/topics/deepseek.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [ai-training](<https://devfeed.tech/tags/ai-training.md>), [collective](<https://devfeed.tech/tags/collective.md>), [communication](<https://devfeed.tech/tags/communication.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deepseek](<https://devfeed.tech/tags/deepseek.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [featured](<https://devfeed.tech/tags/featured.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [gb300-nvl72](<https://devfeed.tech/tags/gb300-nvl72.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [megatron](<https://devfeed.tech/tags/megatron.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [moe](<https://devfeed.tech/tags/moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [train](<https://devfeed.tech/tags/train.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

The article explains how NVIDIA GB300 NVL72 achieved a world record for DeepSeek-V3 671B mixture-of-experts pre-training. It focuses on the communication demands of MoE models, including all-to-all traffic between GPUs, and the need for tightly coupled scale-up and predictable scale-out networking to sustain delivered training performance.

### Source excerpt

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...

## Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills

DevFeed: [Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills](<https://devfeed.tech/articles/post-train-nvidia-cosmos-3-in-one-day-using-agent-skills-6922.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/post-train-nvidia-cosmos-3-in-one-day-using-agent-skills/>)

Author: Tanya Lenz

Published: 2026-07-14T16:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [Agent Skills](<https://devfeed.tech/topics/agent-skills.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [lora](<https://devfeed.tech/topics/lora.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agent-skills](<https://devfeed.tech/tags/agent-skills.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [computer-vision-video-analytics](<https://devfeed.tech/tags/computer-vision-video-analytics.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [lora](<https://devfeed.tech/tags/lora.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>)

### AI overview

A tutorial on post-training NVIDIA Cosmos 3 Nano for video question answering with coding-agent skills, LoRA, and TAO AutoML configuration sweeps.

### Source excerpt

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning...

## Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading

DevFeed: [Reducing High-Bandwidth Memory Bottlenecks in JAX-Based LLM Training with Host Offloading](<https://devfeed.tech/articles/reducing-high-bandwidth-memory-bottlenecks-in-jax-based-llm-training-with-host-offloading-6925.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/reducing-high-bandwidth-memory-bottlenecks-in-jax-based-llm-training-with-host-offloading/>)

Author: Tanya Lenz

Published: 2026-07-10T18:17:40Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [NVLink](<https://devfeed.tech/topics/nvlink.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [blackwell](<https://devfeed.tech/tags/blackwell.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gb200](<https://devfeed.tech/tags/gb200.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grace-cpu](<https://devfeed.tech/tags/grace-cpu.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-techniques](<https://devfeed.tech/tags/llm-techniques.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [nvlink](<https://devfeed.tech/tags/nvlink.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

This article explains how host offloading in JAX-based large language model training reduces GPU high-bandwidth memory pressure by moving selected activations to pinned host memory and streaming them back during the backward pass. It discusses activation-transfer overlap, NVIDIA Grace Blackwell and GB200 NVL72 systems, and experiments involving Llama 3.1 405B and DeepSeek-V3 671B.

### Source excerpt

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,...

## AI Model Co-Design: Hardware-Friendly LLM Design

DevFeed: [AI Model Co-Design: Hardware-Friendly LLM Design](<https://devfeed.tech/articles/ai-model-co-design-hardware-friendly-llm-design-6762.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/ai-model-co-design-hardware-friendly-llm-design/>)

Author: Elizabeth Goodman

Published: 2026-07-10T16:36:02Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [design](<https://devfeed.tech/tags/design.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [featured](<https://devfeed.tech/tags/featured.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [performance](<https://devfeed.tech/tags/performance.md>), [training](<https://devfeed.tech/tags/training.md>), [training-ai-models](<https://devfeed.tech/tags/training-ai-models.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

A practical primer on designing LLMs for modern hardware while balancing accuracy, throughput, and responsiveness. It explains how context length and latency or throughput goals change the importance of attention, feed-forward layers, and parallelism.

### Source excerpt

AI performance comes down to three dimensions: Accuracy: How well the model reasons and produces outputs Throughput: How many tokens per second a...