Groq Alternatives

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Grok is an AI modeled after the Hitchhiker’s Guide to the Galaxy, so intended to answer almost anything and, far harder, even suggest what questions to ask! Grok is designed to answer questions with a bit of wit and has a rebellious streak, so please don’t use it if you hate humor! A unique and fundamental advantage of Grok is that it has real-time knowledge of the world via the 𝕏 platform. It will also answer spicy questions that are rejected by most other AI systems.

OpenRouter is a unified interface for LLMs. OpenRouter scouts for the lowest prices and best latencies/throughputs across dozens of providers, and lets you choose how to prioritize them. No need to change your code when switching between models or providers. You can even let users choose and pay for their own. Evals are flawed; instead, compare models by how often they're used for different purposes. Chat with multiple at once in the chatroom. Model usage can be paid by users, developers, or both, and may shift in availability. You can also fetch models, prices, and limits via API. OpenRouter routes requests to the best available providers for your model, given your preferences. By default, requests are load-balanced across the top providers to maximize uptime, but you can customize how this works using the provider object in the request body. Prioritize providers that have not seen significant outages in the last 10 seconds.

Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, PyTorch and ONNX models. Inference is the process of making predictions using a trained model. In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons. Firstly, standalone GPU instances are typically designed for model training - not for inference. While training jobs batch process hundreds of data samples in parallel, inference jobs usually process a single input in real time, and thus consume a small amount of GPU compute. This makes standalone GPU inference cost-inefficient. On the other hand, standalone CPU instances are not specialized for matrix operations, and thus are often too slow for deep learning inference.

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP).

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.

A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content.

Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.

TorchMetrics is a collection of 90+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. A standardized interface to increase reproducibility. It reduces boilerplate. distributed-training compatible. It has been rigorously tested. Automatic accumulation over batches. Automatic synchronization between multiple devices. You can use TorchMetrics in any PyTorch model, or within PyTorch Lightning to enjoy additional benefits. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. Nearly all functional metrics have a corresponding class-based metric.

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.

AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. The first-generation Inferentia has 8 GB of DDR4 memory per accelerator and also features a large amount of on-chip memory. Inferentia2 offers 32 GB of HBM2e per accelerator, increasing the total memory by 4x and memory bandwidth by 10x over Inferentia.

Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.

Bayesforge™ is a Linux machine image that curates the very best open source software for the data scientist who needs advanced analytical tools, as well as for quantum computing and computational mathematics practitioners who seek to work with one of the major QC frameworks. The image combines common machine learning frameworks, such as PyTorch and TensorFlow, with open source software from D-Wave, Rigetti as well as the IBM Quantum Experience and Google's new quantum computing language Cirq, as well as other advanced QC frameworks. For instance our quantum fog modeling framework, and our quantum compiler Qubiter which can cross-compile to all major architectures. All software is made accessible through the Jupyter WebUI which, due to its modular architecture, allows the user to code in Python, R, and Octave.

Software is what turns a vehicle into an intelligent machine. The NVIDIA DRIVE™ Software stack is open, empowering developers to efficiently build and deploy a variety of state-of-the-art AV applications, including perception, localization and mapping, planning and control, driver monitoring, and natural language processing. The foundation of the DRIVE Software stack, DRIVE OS is the first safe operating system for accelerated computing. It includes NvMedia for sensor input processing, NVIDIA CUDA® libraries for efficient parallel computing implementations, NVIDIA TensorRT™ for real-time AI inference, and other developer tools and modules to access hardware engines. The NVIDIA DriveWorks® SDK provides middleware functions on top of DRIVE OS that are fundamental to autonomous vehicle development. These consist of the sensor abstraction layer (SAL) and sensor plugins, data recorder, vehicle I/O support, and a deep neural network (DNN) framework.

Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.

IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

Traditionally, deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling take days or weeks time. However, with GPUonCLOUD’s dedicated GPU servers, it's a matter of hours. You may want to opt for pre-configured systems or pre-built instances with GPUs featuring deep learning frameworks like TensorFlow, PyTorch, MXNet, TensorRT, libraries e.g. real-time computer vision library OpenCV, thereby accelerating your AI/ML model-building experience. Among the wide variety of GPUs available to us, some of the GPU servers are best fit for graphics workstations and multi-player accelerated gaming. Instant jumpstart frameworks increase the speed and agility of the AI/ML environment with effective and efficient environment lifecycle management.

ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. Develop in your preferred framework without worrying about downstream inferencing implications. ONNX enables you to use your preferred framework with your chosen inference engine. ONNX makes it easier to access hardware optimizations. Use ONNX-compatible runtimes and libraries designed to maximize performance across hardware. Our active community thrives under our open governance structure, which provides transparency and inclusion. We encourage you to engage and contribute.

Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.

Elastic Cloud Server (ECS) provides secure, scalable, on-demand computing resources, enabling you to flexibly deploy applications and workloads. Worry-free comprehensive security protection. Use general computing ECSs, which provide a balance of computing, memory, and network resources. This ECS type is ideal for light- and medium-load applications. Use memory-optimized ECSs, which have a large amount of memory and support ultra-high I/O EVS disks and flexible bandwidths. This ECS type is ideal for applications that process large volumes of data. Use disk-intensive ECSs, which are designed for applications requiring sequential read/write on ultra-large datasets in local storage (such as distributed Hadoop computing) as well as large-scale parallel data processing and log processing. Disk-intensive ECSs are HDD-compatible, feature a default network bandwidth of 10GE, and deliver high PPS and low network latency.

Fortran has been designed from the ground up for computationally intensive applications in science and engineering. Mature and battle-tested compilers and libraries allow you to write code that runs close to the metal, fast. Fortran is statically and strongly typed, which allows the compiler to catch many programming errors early on for you. This also allows the compiler to generate efficient binary code. Fortran is a relatively small language that is surprisingly easy to learn and use. Expressing most mathematical and arithmetic operations over large arrays is as simple as writing them as equations on a whiteboard. Fortran is a natively parallel programming language with intuitive array-like syntax to communicate data between CPUs. You can run almost the same code on a single CPU, on a shared-memory multicore system, or on a distributed-memory HPC or cloud-based system.

DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.

Cloud GPU Service is an elastic computing service that provides GPU computing power with high-performance parallel computing capabilities. As a powerful tool at the IaaS layer, it delivers high computing power for deep learning training, scientific computing, graphics and image processing, video encoding and decoding, and other highly intensive workloads. Improve your business efficiency and competitiveness with high-performance parallel computing capabilities. Set up your deployment environment quickly with auto-installed GPU drivers, CUDA, and cuDNN and preinstalled driver images. Accelerate distributed training and inference by using TACO Kit, an out-of-the-box computing acceleration engine provided by Tencent Cloud.

Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change.

AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data.

Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.

Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure.

The Intel Distribution of OpenVINO toolkit makes it simple to adopt and maintain your code. Open Model Zoo provides optimized, pretrained models and Model Optimizer API parameters make it easier to convert your model and prepare it for inferencing. The runtime (inference engine) allows you to tune for performance by compiling the optimized network and managing inference operations on specific devices. It also auto-optimizes through device discovery, load balancing, and inferencing parallelism across CPU, GPU, and more. Deploy your same application across combinations of host processors and accelerators (CPUs, GPUs, VPUs) and environments (on-premise, on-device, in the browser, or in the cloud).

The developer platform for AI characters. Get a fully integrated platform for AI characters that goes beyond large language models (LLMs), and adds configurable safety, knowledge, memory, narrative controls, multimodality, and more. Craft characters with distinct personalities and contextual awareness that stay in-world or on brand. Seamlessly integrate into real-time applications, with optimization for scale and performance built-in. Optimized for real-time experiences, Inworld offers low-latency interactions that scale with your application. Orchestrating across LLMs allows us to deliver high-quality interactions with faster inference and lower costs. Every interaction has a context and models need to be aware of yours. Add custom knowledge, content and safety guardrails, and narrative controls to keep your AI in character, in-world, or on brand. Put personality at the center of your AI. Our multimodal AI mimics the full range of human expression.

Exafunction optimizes your deep learning inference workload, delivering up to a 10x improvement in resource utilization and cost. Focus on building your deep learning application, not on managing clusters and fine-tuning performance. In most deep learning applications, CPU, I/O, and network bottlenecks lead to poor utilization of GPU hardware. Exafunction moves any GPU code to highly utilized remote resources, even spot instances. Your core logic remains an inexpensive CPU instance. Exafunction is battle-tested on applications like large-scale autonomous vehicle simulation. These workloads have complex custom models, require numerical reproducibility, and use thousands of GPUs concurrently. Exafunction supports models from major deep learning frameworks and inference runtimes. Models and dependencies like custom operators are versioned so you can always be confident you’re getting the right results.

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. : Luminoth is still alpha-quality release, which means the internal and external interfaces (such as command line) are very likely to change as the codebase matures. . If you want GPU support, you should install the GPU version of TensorFlow with pip install tensorflow-gpu, or else you can use the CPU version using pip install tensorflow. Luminoth can also install TensorFlow for you if you install it with pip install luminoth[tf] or pip install luminoth[tf-gpu], depending on the version of TensorFlow you wish to use.

Conventional CPUs can no longer cope with the increased demand for computing power. GPU processors exceed the data processing speed of conventional CPUs by 100-200 times. We provide servers that are specifically designed for machine learning and deep learning purposes and are equipped with distinctive features. Modern hardware based on the NVIDIA® GPU chipset, which has a high operation speed. The newest Tesla® V100 cards with their high processing power. Optimized for deep learning software, TensorFlow™, Caffe2, Torch, Theano, CNTK, MXNet™. Includes development tools based on the programming languages ​​Python 2, Python 3, and C++. We do not charge fees for every extra service. This means disk space and traffic are already included in the cost of the basic services package. In addition, our servers can be used for various tasks of video processing, rendering, etc. LeaderGPU® customers can now use a graphical interface via RDP out of the box.

Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT-3.5 on most standard benchmarks.

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

RoBERTa builds on BERT’s language masking strategy, wherein the system learns to predict intentionally hidden sections of text within otherwise unannotated language examples. RoBERTa, which was implemented in PyTorch, modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. This allows RoBERTa to improve on the masked language modeling objective compared with BERT and leads to better downstream task performance. We also explore training RoBERTa on an order of magnitude more data than BERT, for a longer amount of time. We used existing unannotated NLP datasets as well as CC-News, a novel set drawn from public news articles.

Impacting virtually every industry, generative AI unlocks a new frontier of opportunities, for knowledge and creative workers, to solve today’s most important challenges. NVIDIA is powering generative AI through an impressive suite of cloud services, pre-trained foundation models, as well as cutting-edge frameworks, optimized inference engines, and APIs to bring intelligence to your enterprise applications. NVIDIA AI Foundations is a set of cloud services that advance enterprise-level generative AI and enable customization across use cases in areas such as text (NVIDIA NeMo™), visual content (NVIDIA Picasso), and biology (NVIDIA BioNeMo™). Unleash the full potential with NeMo, Picasso, and BioNeMo cloud services, powered by NVIDIA DGX™ Cloud, the AI supercomputer. Marketing copy, storyline creation, and global translation in many languages. For news, email, meeting minutes, and information synthesis.

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords. The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.

Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings.

OptiSol that offers AI-powered document analysis solutions. OptiSol helps businesses transform data into insights using a variety of technologies, including natural language processing and machine learning. They offer a variety of services, including document comprehension, visual comprehension, and natural language inference. OptiSol can be integrated into existing applications and can be used in a variety of industries.

Virtuoso Universal Server is a modern platform built on existing open standards that harnesses the power of Hyperlinks ( functioning as Super Keys ) for breaking down data silos that impede both user and enterprise ability. Using Virtuoso, you can easily generate financial profile knowledge graphs from near real time financial activity that reduce the cost and complexity associated with detecting fraudent activity patterns. Courtesy of its high-performance, secure, and scalable dbms engine, you can use intelligent reasoning and inference to harmonize fragmented identities using personally identifying attributes such as email addresses, phone numbers, social-security numbers, drivers licenses, etc. for building fraud detection solutions. Virtuoso helps you build powerful solutions applications driven by knowledge graphs derived from a variety of life sciences oriented data sources.

We are developing efficient large language models for India's diverse linguistic culture and enabling new GenAI applications through bespoke enterprise models. We are building an enterprise-grade platform that lets you develop and evaluate your company’s GenAI apps. We believe in the power of open-source to accelerate AI innovation and will be contributing to open-source models and datasets, as well be leading efforts for large-scale data curation in public-good space. We are a dynamic and close-knit team of AI pioneers, blending expertise in research, engineering, product design, and business operations. Our diverse backgrounds unite under a shared commitment to excellence in science and the creation of societal impact. We foster an environment where tackling complex tech challenges is not just a job, but a passion.