developer.nvidia.comadded August 2026
Nvidia Developer Blogby Tanya Lenz28d ago
Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill and decode stages.
Nvidia Developer Blogby Jonathan Bentz28d ago
Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software platform.
Nvidia Developer Blogby Elizabeth Goodman8 Sept 2026
In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and NVIDIA will be growing and maturing CUDA...
Nvidia Developer Blogby Tanya Lenz4 Sept 2026
Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it before contributing.
Nvidia Developer Blogby Elizabeth Goodman4 Sept 2026
Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run locally on edge hardware.
Nvidia Developer Blogby Elizabeth Goodman3 Sept 2026
Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook where that data resides, and invoke...
Nvidia Developer Blogby Tanya Lenz3 Sept 2026
AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents.
Nvidia Developer Blogby Elizabeth Goodman2 Sept 2026
NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training.
Nvidia Developer Blogby Tanya Lenz2 Sept 2026
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 offers five guidelines for selecting...
Nvidia Developer Blogby Michelle Horton1 Sept 2026
AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons.
Nvidia Developer Blogby Elizabeth Goodman1 Sept 2026
The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently size GPU resources for inference...
Nvidia Developer Blogby Michelle Horton31 Aug 2026
Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.
Nvidia Developer Blogby Michelle Horton31 Aug 2026
A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline—for example, from an SUV to a sedan or another vehicle variant in the portfolio—and its...
Nvidia Developer Blogby Tanya Lenz28 Aug 2026
Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing, post-processing, and runtime code.
Nvidia Developer Blogby Farshad Ghodsian26 Aug 2026
AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads, hyperscalers and AI-native companies are...
Nvidia Developer Blogby Tanya Lenz26 Aug 2026
Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to continuously localize the robot...
Nvidia Developer Blogby Michelle Horton26 Aug 2026
Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate.
Nvidia Developer Blogby Michelle Horton26 Aug 2026
Alibaba released the model weights for Qwen3.8-Flash-Next as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate.
Nvidia Developer Blogby Michelle Horton25 Aug 2026
When an LLM engine process fails, the standard recovery path involves a cold restart. This requires loading weights into HBM from storage, compiling kernels, and capturing NVIDIA CUDA graphs.
Nvidia Developer Blogby Elizabeth Goodman25 Aug 2026
For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain, and maintain bindings back to Python...
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