NVIDIA Developer Blog

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NVIDIA Developer Blog official-blog 5mo ago

Implementing Falcon-H1 Hybrid Architecture in NVIDIA Megatron Core

In the rapidly evolving landscape of large language model (LLM) development, NVIDIA Megatron Core has emerged as the foundational framework for training massive...

14
NVIDIA Developer Blog official-blog 5mo ago

Enhancing Distributed Inference Performance with the NVIDIA Inference Transfer Library

Deploying large language models (LLMs) requires large-scale distributed inference, which spreads model computation and request handling across many GPUs and...

32
NVIDIA Developer Blog official-blog 5mo ago

Removing the Guesswork from Disaggregated Serving

Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem. The ideal...

37
NVIDIA Developer Blog official-blog 5mo ago

Tuning Flash Attention for Peak Performance in NVIDIA CUDA Tile

In this post, we dive into one of the most critical workloads in modern AI: Flash Attention, where you’ll learn: How to implement Flash Attention using NVIDIA...

37
NVIDIA Developer Blog official-blog 5mo ago

Controlling Floating-Point Determinism in NVIDIA CCCL

A computation is considered deterministic if multiple runs with the same input data produce the same bitwise result. While this may seem like a simple property...

32
NVIDIA Developer Blog official-blog 5mo ago

How to Minimize Game Runtime Inference Costs with Coding Agents

NVIDIA ACE is a suite of technologies for building AI agents for gaming. ACE provides ready-to-integrate cloud and on-device AI models for every part of in-game...

23
NVIDIA Developer Blog official-blog 5mo ago

cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia

NVIDIA CUDA Tile is one of the most significant additions to NVIDIA CUDA programming and unlocks automatic access to tensor cores and other specialized...

29
NVIDIA Developer Blog official-blog 5mo ago

Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

Autonomous networks are quickly becoming one of the top priorities in telecommunications. According to the latest NVIDIA State of AI in Telecommunications...

25
NVIDIA Developer Blog official-blog 5mo ago

5 New Digital Twin Products Developers Can Use to Build 6G Networks

To make 6G a reality, the telecom industry must overcome a fundamental challenge: how to design, train, and validate AI-native networks that are too complex to...

7
NVIDIA Developer Blog official-blog 5mo ago

Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints

Alibaba has introduced the new open source Qwen3.5 series built for native multimodal agents. The first model in this series is a ~400B parameter native...

25
NVIDIA Developer Blog official-blog 5mo ago

Maximizing GPU Utilization with NVIDIA Run:ai and NVIDIA NIM

Organizations deploying LLMs are challenged by inference workloads with different resource requirements. A small embedding model might use only a few gigabytes...

27
NVIDIA Developer Blog official-blog 5mo ago

Making Softmax More Efficient with NVIDIA Blackwell Ultra

LLM context lengths are exploding, and architectures are moving toward complex attention schemes like Multi-Head Latent Attention (MLA) and Grouped Query...

9
NVIDIA Developer Blog official-blog 5mo ago

Using NVFP4 Low-Precision Model Training for Higher Throughput Without Losing Accuracy

As the sizes of AI models and datasets continue to increase, relying only on higher-precision BF16 training is no longer sufficient. Key challenges such as...

25
NVIDIA Developer Blog official-blog 5mo ago

Accelerating Data Processing with NVIDIA Multi-Instance GPU and Locality Domains

NVIDIA flagship data center GPUs in the NVIDIA Ampere, NVIDIA Hopper, and NVIDIA Blackwell families all feature non-uniform memory access (NUMA) behaviors, but...

30
NVIDIA Developer Blog official-blog 5mo ago

Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai

As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges...

30
NVIDIA Developer Blog official-blog 5mo ago

Topping the GPU MODE Kernel Leaderboard with NVIDIA cuda.compute

Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to...

6
NVIDIA Developer Blog official-blog 5mo ago

How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models

As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost...

34
NVIDIA Developer Blog official-blog 5mo ago

Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities

Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms,...

6
NVIDIA Developer Blog official-blog 6mo ago

R²D²: Scaling Multimodal Robot Learning with NVIDIA Isaac Lab

Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physical world is expensive, slow, and often...

27
NVIDIA Developer Blog official-blog 6mo ago

Using Accelerated Computing to Live-Steer Scientific Experiments at Massive Research Facilities

Scientists and engineers who design and build unique scientific research facilities face similar challenges. These include managing massive data rates that...

5
NVIDIA Developer Blog official-blog 6mo ago

Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy

NVIDIA TensorRT LLM enables developers to build high-performance inference engines for large language models (LLMs), but deploying a new architecture...

31
NVIDIA Developer Blog official-blog 6mo ago

3 Ways NVFP4 Accelerates AI Training and Inference

The latest AI models continue to grow in size and complexity, demanding increasing amounts of compute performance for training and inference—far beyond what...

14
NVIDIA Developer Blog official-blog 6mo ago

How to Build License-Compliant Synthetic Data Pipelines for AI Model Distillation

Specialized AI models are built to perform specific tasks or solve particular problems. But if you’ve ever tried to fine-tune or distill a domain-specific...

18
NVIDIA Developer Blog official-blog 6mo ago

How Painkiller RTX Uses Generative AI to Modernize Game Assets at Scale

Painkiller RTX sets a new standard for how small teams can balance massive visual ambition with limited resources by integrating generative AI. By upscaling...

35

Implementing Falcon-H1 Hybrid Architecture in NVIDIA Megatron Core

Enhancing Distributed Inference Performance with the NVIDIA Inference Transfer Library

Removing the Guesswork from Disaggregated Serving

Tuning Flash Attention for Peak Performance in NVIDIA CUDA Tile

Controlling Floating-Point Determinism in NVIDIA CCCL

How to Minimize Game Runtime Inference Costs with Coding Agents

cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia

Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

5 New Digital Twin Products Developers Can Use to Build 6G Networks

Develop Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints

Maximizing GPU Utilization with NVIDIA Run:ai and NVIDIA NIM

Making Softmax More Efficient with NVIDIA Blackwell Ultra

Using NVFP4 Low-Precision Model Training for Higher Throughput Without Losing Accuracy

Accelerating Data Processing with NVIDIA Multi-Instance GPU and Locality Domains

Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai

Topping the GPU MODE Kernel Leaderboard with NVIDIA cuda.compute

How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models

Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities

R²D²: Scaling Multimodal Robot Learning with NVIDIA Isaac Lab

Using Accelerated Computing to Live-Steer Scientific Experiments at Massive Research Facilities

Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy

3 Ways NVFP4 Accelerates AI Training and Inference

How to Build License-Compliant Synthetic Data Pipelines for AI Model Distillation

How Painkiller RTX Uses Generative AI to Modernize Game Assets at Scale