Alternatives to ThirdAI

Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

This tool is a model quantization tool to convolution neural networks (CNN). This tool can quantify both weights/biases as well as activations in 32-bit floating point (FP32) and 8-bit integer (INT8) formats, or any other bit depths. This tool can increase the inference performance and efficiency by ensuring accuracy. This tool supports all common layers in neural networks: convolution, pooling and fully-connected. It also supports batch normalization. Quantization tools do not require retraining the network or labeled data sets. Only one batch of photos is required. The process takes a few seconds to several hours depending on the size and complexity of the neural network. This allows for rapid model updates. This tool is collaboratively optimized for DeePhi DPU. It could generate INT8 format model file files required by DNNC.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

DeepCube is a company that focuses on deep learning technologies. This technology can be used to improve the deployment of AI systems in real-world situations. The company's many patent innovations include faster, more accurate training of deep-learning models and significantly improved inference performance. DeepCube's proprietary framework is compatible with any hardware, datacenters or edge devices. This allows for over 10x speed improvements and memory reductions. DeepCube is the only technology that allows for efficient deployment of deep-learning models on intelligent edge devices. The model is typically very complex and requires a lot of memory. Deep learning deployments today are restricted to the cloud because of the large amount of memory and processing requirements.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

NeuroIntelligence, a software application for neural networks, is designed to help experts in data mining, predictive modeling, pattern recognition, and neural network design in solving real-world problems. NeuroIntelligence uses only proven neural net modeling algorithms and techniques. It is easy to use and fast. Visualized architecture search, training and testing of neural networks. Neural network architecture search. Fitness bars. Network training graphs comparison. Training graphs, dataset error and network error, weights distribution, neural network input importance, and errors distribution Testing, actual vs. output graph, scatter plot and response graph, ROC curve and confusion matrix. NeuroIntelligence's interface is optimized to solve data mining and forecasting, classification, and pattern recognition problems. The tool's intuitive GUI and time-saving features make it easy to create a better solution faster.

Tenstorrent DevCloud was created to allow people to test their models on our servers, without having to purchase our hardware. Tenstorrent AI is being built in the cloud to allow programmers to test our AI solutions. After logging in, your first login is free. You can then connect with our team to better assess your needs. Tenstorrent is a group of motivated and competent people who have come together to create the best computing platform for AI/software 2.0. Tenstorrent is a new-generation computing company that aims to address the rapidly increasing computing needs for software 2.0. Tenstorrent is based in Toronto, Canada. It brings together experts in the fields of computer architecture, basic design and neural network compilers. ur processors have been optimized for neural network training and inference. They can also perform other types of parallel computation. Tenstorrent processors are made up of a grid consisting of Tensix cores.

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

Zyphra, an artificial intelligence company with offices in Palo Alto and Montreal, is growing in London. We're developing MaiaOS, an agent system that combines advanced research in next-gen neuronal network architectures (SSM-hybrids), long-term memories & reinforcement learning. We believe that the future of AGI is a combination of cloud-based and on-device strategies, with an increasing shift towards local inference. MaiaOS was built around a deployment platform that maximizes the efficiency of inference for real-time Intelligence. Our AI and product teams are drawn from top organizations and institutions, including Google DeepMind and Anthropic. They also come from Qualcomm, Neuralink and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. The Zyphra team is dedicated to democratizing advanced artificial intelligence systems.

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

A powerful, flexible, intuitive framework for neural networks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be used on multiple GPUs without much effort. Chainer supports a variety of network architectures, including convnets, feed-forward nets, and recurrent nets. It also supports per batch architectures. Forward computation can include any control flow statement of Python without sacrificing the ability to backpropagate. It makes code easy to understand and debug. ChainerRLA is a library that implements several state-of-the art deep reinforcement algorithms. ChainerCVA is a collection that allows you to train and run neural network for computer vision tasks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be run on multiple GPUs without much effort.

Neuralhub is an AI system that simplifies the creation, experimentation, and innovation of neural networks. It helps AI enthusiasts, researchers, engineers, and other AI professionals. Our mission goes beyond just providing tools. We're creating a community where people can share and collaborate. We want to simplify deep learning by bringing together all the tools, models, and research into a collaborative space. This will make AI research, development, and learning more accessible. Create a neural network by starting from scratch, or use our library to experiment and create something new. Construct your neural networks with just one click. Visualize and interact with each component of the network. Tune hyperparameters like epochs and features, labels, and more.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

AForge.NET is an open-source C# framework for researchers and developers in the fields of Computer Vision, Artificial Intelligence - image processors, neural networks, genetic algorithms and fuzzy logic, as well as machine learning and robotics. The framework's development is ongoing, which means that new features and namespaces are being added constantly. You can track the source repository's log to keep track of its progress or visit the project discussion group to receive the most recent information. The framework comes with many examples of applications that demonstrate how to use it, as well as different libraries and their source.

Breaking the limits of AI processors and edge AI acceleration. EdgeCortix AI cores are the answer to AI inference acceleration that requires more TOPS, less latency, greater area and power efficiency and scalability. Developers can choose from a variety of general-purpose processor cores including CPUs and GPUs. These general-purpose cores are not suited to deep neural network workloads. EdgeCortix was founded with the mission of redefining AI processing at the edge from scratch. EdgeCortix technology, which includes a full-stack AI-inference software development environment, reconfigurable edge AI-inference IP at run-time, and edge AI-chips for boards and systems, allows designers to deploy AI performance near cloud-level at the edge. Imagine what this could do for these applications and others. Finding threats, increasing situational awareness, making vehicles smarter.

NVIDIA Picasso, a cloud service that allows you to build generative AI-powered visual apps, is available. Software creators, service providers, and enterprises can run inference on models, train NVIDIA Edify foundation model models on proprietary data, and start from pre-trained models to create image, video, or 3D content from text prompts. The Picasso service is optimized for GPUs. It streamlines optimization, training, and inference on NVIDIA DGX Cloud. Developers and organizations can train NVIDIA Edify models using their own data, or use models pre-trained by our premier partners. Expert denoising network to create photorealistic 4K images The novel video denoiser and temporal layers generate high-fidelity videos with consistent temporality. A novel optimization framework to generate 3D objects and meshes of high-quality geometry. Cloud service to build and deploy generative AI-powered image and video applications.

NVIDIA NIM's microservices allow you to deploy AI agents anywhere, while allowing you to explore the latest AI models. NVIDIA NIM provides a set easy-to-use microservices for inference that allows the deployment of foundational models across any data center or cloud, while ensuring data security. NVIDIA AI also provides access to the Deep Learning Institute, which offers technical training for AI, data science and accelerated computing. AI models produce responses and outputs that are based on complex machine learning algorithms. These responses or outputs can be inaccurate, harmful or indecent. By testing this model you accept all risk for any harm caused by the model's output or response. Please do not upload confidential or personal information unless explicitly permitted. Your use is recorded for security reasons.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

InferKit provides a web interface as well as an API to create AI-based text generators. There's something for everyone, whether you're an app developer or a novelist looking to find inspiration. InferKit's text generator takes the text you provide and generates what it thinks is next using a state of the art neural network. It can generate any length of text on virtually any topic and is configurable. You can use the tool via the web interface or through the developer API. Register now to get started. You can also use the network to write poetry or stories. Marketing and auto-completion are other possible uses. The generator can only understand a limited amount of text at once (currently, at most 3000 characters), so if you give it a longer prompt it will not use the beginning. The network is already trained and doesn't learn from inputs. Each request must contain at least 100 characters

NetMind.AI, a decentralized AI ecosystem and computing platform, is designed to accelerate global AI innovations. It offers AI computing power that is affordable and accessible to individuals, companies, and organizations of any size by leveraging idle GPU resources around the world. The platform offers a variety of services including GPU rental, serverless Inference, as well as an AI ecosystem that includes data processing, model development, inference and agent development. Users can rent GPUs for competitive prices, deploy models easily with serverless inference on-demand, and access a variety of open-source AI APIs with low-latency, high-throughput performance. NetMind.AI allows contributors to add their idle graphics cards to the network and earn NetMind Tokens. These tokens are used to facilitate transactions on the platform. Users can pay for services like training, fine-tuning and inference as well as GPU rentals.

YandexART, a diffusion neural net by Yandex, is designed for image and videos creation. This new neural model is a global leader in image generation quality among generative models. It is integrated into Yandex's services, such as Yandex Business or Shedevrum. It generates images and video using the cascade diffusion technique. This updated version of the neural network is already operational in the Shedevrum app, improving user experiences. YandexART, the engine behind Shedevrum, boasts a massive scale with 5 billion parameters. It was trained on a dataset of 330,000,000 images and their corresponding text descriptions. Shedevrum consistently produces high-quality content through the combination of a refined dataset with a proprietary text encoding algorithm and reinforcement learning.

Platform with NVIDIA H100 Tensor core GPUs. Competitive pricing. Support from a dedicated team. Built for large-scale ML workloads. Get the most from multihost training with thousands of H100 GPUs in full mesh connections using the latest InfiniBand networks up to 3.2Tb/s. Best value: Save up to 50% on GPU compute when compared with major public cloud providers*. You can save even more by purchasing GPUs in large quantities and reserving GPUs. Onboarding assistance: We provide a dedicated engineer to ensure smooth platform adoption. Get your infrastructure optimized, and k8s installed. Fully managed Kubernetes - Simplify the deployment and scaling of ML frameworks using Kubernetes. Use Managed Kubernetes to train GPUs on multiple nodes. Marketplace with ML Frameworks: Browse our Marketplace to find ML-focused libraries and applications, frameworks, and tools that will streamline your model training. Easy to use. All new users are entitled to a one-month free trial.

The best platform for the entire lifecycle of computer vision. You can go from image annotation to precise neural networks in 10x less time. Our best-in-class data labeling software transforms images, videos, and 3D point clouds into high-quality training data. You can train your models, track experiments and visualize the results. Our self-hosted solution guarantees data privacy, powerful customization capabilities and easy integration into any technology stack. Computer Vision is a turnkey solution: multi-format data management, quality control at scale, and neural network training in an end-to-end platform. Professional video editing software created by data scientists for data science -- the most powerful tool for machine learning and other purposes.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

ConvNetJS is a Javascript library that allows you to train deep learning models (neural network) in your browser. You can train by simply opening a tab. No software requirements, no compilers, no installations, no GPUs, no sweat. The library was originally created by @karpathy and allows you to create and solve neural networks using Javascript. The library has been greatly expanded by the community, and new contributions are welcome. If you don't want to develop, this link to convnet.min.js will allow you to download the library as a plug-and play. You can also download the latest version of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create an index.html file with no content and copy build/convnet.min.js to that folder.

Amazon EC2 capacity blocks for ML allow you to reserve accelerated compute instance in Amazon EC2 UltraClusters that are dedicated to machine learning workloads. This service supports Amazon EC2 P5en instances powered by NVIDIA Tensor Core GPUs H200, H100 and A100, as well Trn2 and TRn1 instances powered AWS Trainium. You can reserve these instances up to six months ahead of time in cluster sizes from one to sixty instances (512 GPUs, or 1,024 Trainium chip), providing flexibility for ML workloads. Reservations can be placed up to 8 weeks in advance. Capacity Blocks can be co-located in Amazon EC2 UltraClusters to provide low-latency and high-throughput connectivity for efficient distributed training. This setup provides predictable access to high performance computing resources. It allows you to plan ML application development confidently, run tests, build prototypes and accommodate future surges of demand for ML applications.

We have developed and are open-sourcing Whisper, a neural network that approximates human-level robustness in English speech recognition. Whisper is an automated speech recognition (ASR), system that was trained using 680,000 hours of multilingual, multitask supervised data from the internet. The use of such a diverse dataset results in a better resistance to accents, background noise, technical language, and other linguistic issues. It also allows transcription in multiple languages and translation from these languages into English. We provide inference code and open-sourcing models to help you build useful applications and further research on robust speech processing. The Whisper architecture is an end-to-end, simple approach that can be used as an encoder/decoder Transformer. The input audio is divided into 30-second chunks and converted into a log Mel spectrogram. This then goes into an encoder.

The basic unit of the neural system is the neuron. They can be connected to other neurons or gate connections between neurons. This allows you to create flexible and complex architectures. Trainers can use any training set and take any network, regardless of its architecture. It also includes tasks to test networks such as learning an XOR or completing a Discrete Sequence Recall task. You can import/export networks to JSON, convert them to workers, or use standalone functions. They can be connected with other networks or gate connections. The Architect has built-in useful architectures like multilayer perceptrons and multilayer long-term memory networks (LSTM), liquid states machines, and Hopfield networks. You can also optimize, extend, export to JSON, convert to Workers or standalone Functions, and even clone networks. A network can be used to project a connection to another or to gate a connection between two networks.

Fido is an open-source, lightweight, modular C++ machine-learning library. The library is geared towards embedded electronics and robotics. Fido contains implementations of reinforcement learning methods, genetic algorithms and trainable neural networks. It also includes a full-fledged robot simulator. Fido also includes a human-trainable robot controller system, as described by Truell and Gruenstein. Although the simulator is not available in the latest release, it can still be downloaded to experiment on the simulator branch.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

Cogniac's no code solution allows organizations to take advantage of the latest developments in Artificial Intelligence and convolutional neural network technology to deliver extraordinary operational performance. Cogniac's AI platform for machine vision enables enterprises to reach Industry 4.0 standards via visual data management and automated automation. Cogniac helps organizations' operations divisions deliver smart continuous improvement. Cogniac's user interface was designed to be used by non-technical users. The Cogniac platform's drag-and-drop nature allows subject matter experts and other specialists to concentrate on the tasks that are most important. Cogniac can detect defects in as few as 100 images. After being trained with 25 approved images and 75 deficient images, Cogniac AI can deliver results comparable to human subject matter experts within hours.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

Generative AI has a profound impact on virtually every industry. It opens up new opportunities for creative workers and knowledge to solve the world's most pressing problems. NVIDIA is empowering generative AI with a powerful suite of cloud services, pretrained foundation models, cutting-edge frameworks and optimized inference engines. NVIDIA AI Foundations is an array of cloud services that enable customization across use cases in areas like text (NVIDIA NeMo™, NVIDIA Picasso), or biology (NVIDIA BIONeMo™. Enjoy the full potential of NeMo, Picasso and BioNeMo cloud-based services powered by NVIDIA DGX™ Cloud, an AI supercomputer. Marketing copy, storyline creation and global translation in many different languages. News, email, meeting minutes and information synthesis.

NetApp AIPod is an advanced AI infrastructure solution designed to simplify the deployment and management of artificial intelligence workflows. Combining NVIDIA-validated systems like DGX BasePOD™ with NetApp’s cloud-connected all-flash storage, it offers a unified platform for analytics, training, and inference. This scalable solution enables organizations to accelerate AI adoption, streamline data workflows, and ensure seamless integration across hybrid cloud environments. With preconfigured, optimized infrastructure, AIPod reduces operational complexity and helps businesses gain insights faster while maintaining robust data security and management capabilities.

We have built the fastest AI acceleration, based on one of the largest processors in the industry. It is also easy to use. Cerebras' blazingly fast training, ultra-low latency inference and record-breaking speed-to-solution will help you achieve your most ambitious AI goals. How ambitious is it? How ambitious?

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Production-ready AI models that are fast and accurate. High-availability inference API that leverages the most advanced NVIDIA GPUs. We have selected the most popular open-source natural language processing models (NLP) and deployed them for the community. You can fine-tune your models (including GPT-J) or upload your custom models. Then, deploy them to production. Upload your AI models, including GPT-J, to your dashboard and immediately use them in production.

A platform that manages model, project, and hosting. This platform allows companies to transform data and algorithms into comprehensive, execution-ready AI strategies. Securely build, train, and manage models. You can create products that use AI models from anywhere and at any time. Reduce the risks associated with AI investments while increasing strategic flexibility. Fully automated model testing, evaluation deployment and scaling. From real-time, low latency, high-throughput, inference to batch-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. A user-friendly interface that serves as a central hub for managing projects, visualizing data, and training models through collaborative and reproducible workflows.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

Nebula enables you to scale and deploy your AI projects quickly and easily2 on a highly robust GPU infrastructure. Nebula Cloud powered by Nvidia GPUs on demand allows you to train and infer models easily and securely. You can also create and manage containerized workloads using Nebula's easy-to-use orchestration layer. Access Nebula’s MLOps, low-code/no code engines and AI-powered applications to quickly and seamlessly deploy AI-powered apps for business teams. Choose from the Nebula containerized AI Cloud, your on-prem or any cloud. The Nebula Unify platform allows you to build and scale AI-enabled use cases for business in a matter weeks, not months.

Navigator provides rapid, location-independent answers to users, allowing them to create custom AI models that meet their individual needs. Experience innovation when technology complements human expertise. Create, manage, and watch content collaboratively with AI, co-workers and friends. Create custom AI models within minutes, not hours. Revitalize large models by streamlining training, reducing compute costs and incorporating attention steering. It seamlessly translates user interaction into manageable tasks. It chooses and executes AI models that are most appropriate for each task. The responses it delivers are in line with the user's expectations. No back doors, distributed storage and seamless inference. It uses distributed, edge-friendly technologies for lightning-fast interaction, wherever you are. Join our vibrant distributed storage eco-system to unlock access to the first watermarked universal models dataset.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

Outspeed provides networking infrastructure and inference infrastructure for building fast, real-time AI voice and video apps. AI-powered speech and natural language processing for intelligent voice assistants. Automated transcription and voice-controlled system. Create interactive digital characters to be used as virtual hosts, AI tutors or customer service. Real-time animations and natural conversations are key to engaging digital interactions. Real-time AI visual for quality control, surveillance and touchless interaction. High-speed and accurate processing and analysis of video streams and images. AI-driven content generation for creating vast, detailed digital worlds efficiently. Ideal for virtual reality, architectural visualizations and game environments. Adapt's flexible SDK, infrastructure and SDK allows you to create custom multimodal AI solutions. Combine AI models, data and interaction modes to create innovative applications.

A system that can scale to millions of users, without requiring an engineering team. Create, customize and deploy your chat-based AI. Finance chatbot. xFinance is a 13-billion-parameter model fine-tuned using LoRA. Our goal was show that impressive results can be achieved in financial NLP without breaking the bank. Your own AI assistant to chat with documents. Single or multiple documents. Simple or complex questions. Easy-to-use deep learning platform, hardware efficient algorithms that speed up inference and lower costs. Real-time monitoring and logging of resource usage and cloud costs for deployed models. xTuring, an open-source AI software for personalization, is a powerful tool. xTuring provides a simple interface for personalizing LLMs based on your data and application.