NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
NVIDIA and Google Cloud have introduced new AI infrastructure and tools for users to build and deploy huge models for generative AI and accelerate data science workloads.
Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang spoke in a fireside chat at Google Cloud Next about how the partnership is bringing end-to-end machine learning services to some of the biggest AI customers in the world, including by making it simple to run AI supercomputers with Google Cloud offerings based on NVIDIA technologies.
The Google DeepMind and Google research teams have been using the same NVIDIA technology for the past two years in their new hardware and software integrations.
Google Cloud has a long history of innovating in AI to foster and speed innovation for our customers,” Kurian said. “Many of Google’s products are built and served on NVIDIA GPUs, and many of our customers are seeking out NVIDIA accelerated computing to power efficient development of LLMs to advance generative AI.”
NVIDIA Integrations to Speed AI and Data Science Development.
Google’s framework for building massive large language models (LLMs), PaxML, is now optimized for NVIDIA accelerated computing.
Originally built to span multiple Google TPU accelerator slices, PaxML now enables developers to use NVIDIA® H100 and A100 Tensor Core GPUs for advanced and fully configurable experimentation and scale. A GPU-optimized PaxML container is available immediately in the NVIDIA NGC™ software catalog. In addition, PaxML runs on JAX, which has been optimized for GPUs leveraging the OpenXLA compiler.
Google DeepMind and other Google researchers are among the first to use PaxML with NVIDIA GPUs for exploratory research.
The NVIDIA-optimized container for PaxML will be available immediately on the NVIDIA NGC container registry to researchers, startups and enterprises worldwide that are building the next generation of AI-powered applications.
Additionally, the companies announced Google’s integration of serverless Spark with NVIDIA GPUs through Google’s Dataproc service. This will help data scientists speed Apache Spark workloads to prepare data for AI development.
These new integrations are the latest in NVIDIA and Google’s extensive history of collaboration.
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