Two new open-weight AI reasoning models from OpenAI released today bring cutting-edge AI development directly into the hands of developers, enthusiasts, enterprises, startups and governments everywhere — across every industry and at every scale. NVIDIA’s collaboration with OpenAI on these open models — gpt-oss-120b and gpt-oss-20b — is a testament to the power of community-driven
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NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA DGX back in 2016. The collaborative AI innovation continues with the OpenAI…
NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA DGX back in 2016. The collaborative AI innovation continues with the OpenAI gpt-oss-20b and gpt-oss-120b launch. NVIDIA has optimized both new open-weight models for accelerated inference performance on NVIDIA Blackwell architecture, delivering up to 1.5 million tokens per second (TPS) on an NVIDIA GB200 NVL72 system.
NVIDIA and OpenAI began pushing the boundaries of AI with the launch of NVIDIA DGX back in 2016. The collaborative AI innovation continues with the OpenAI gpt-oss-20b and gpt-oss-120b launch. NVIDIA has optimized both new open-weight models for accelerated inference performance on NVIDIA Blackwell architecture, delivering up to 1.5 million tokens per second (TPS) on an NVIDIA GB200 NVL72 system.
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NVIDIA GPUs are at the heart of modern computing. They’re used across industries—from healthcare and finance to scientific research, autonomous systems, and AI infrastructure. NVIDIA GPUs are embedded into CT scanners and MRI machines, DNA sequencers, air traffic RADAR tracking systems, city traffic management systems, self-driving cars, supercomputers, TV broadcasting systems to casino machines and
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Many CUDA kernels are bandwidth bound, and the increasing ratio of flops to bandwidth in new hardware results in more bandwidth bound kernels. This makes it…
If you’ve used the NVIDIA CUDA Compiler (NVCC) for your NVIDIA GPU application recently, you may have encountered a warning message like the following: nvcc…
If you’ve used the NVIDIA CUDA Compiler (NVCC) for your NVIDIA GPU application recently, you may have encountered a warning message like the following: What does this mean exactly, and what actions should you take? In this post, we’ll explain how the NVIDIA CUDA Toolkit and NVIDIA Driver work together to support GPUs The software stack for programming GPUs is divided into two…
NVIDIA CUDA-Q 0.12 introduces new simulation tools for accelerating how researchers develop quantum applications and design performant quantum hardware. With…
NVIDIA CUDA-Q 0.12 introduces new simulation tools for accelerating how researchers develop quantum applications and design performant quantum hardware. With the new API, users can obtain more detailed statistics on individual runs (or shots) of a simulation, rather than being restricted to aggregated statistical outputs from simulations. Access to raw shot data is important to researchers…
A key challenge for retrieval-augmented generation (RAG) systems is handling user queries that lack explicit clarity or carry implicit intent. Users often…
A key challenge for retrieval-augmented generation (RAG) systems is handling user queries that lack explicit clarity or carry implicit intent. Users often phrase questions imprecisely. For instance, consider the user query, “Tell me about the latest update in NVIDIA NeMo model training.” It’s possible that the user is implicitly interested in advancements in NeMo large language model (LLM)…
You’ve been there. You wrote the perfect Python script, tested it on a sample CSV, and everything worked flawlessly. But when you unleashed it on the full 10…
You’ve been there. You wrote the perfect Python script, tested it on a sample CSV, and everything worked flawlessly. But when you unleashed it on the full 10 million row dataset, your laptop fan started screaming, your console froze, and you had enough time to brew three pots of coffee before seeing a result. What if you could get massive speedups on those exact same workflows with a simple…