Alternatives to Exafunction
Compare Exafunction alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Exafunction in 2025. Compare features, ratings, user reviews, pricing, and more from Exafunction competitors and alternatives in order to make an informed decision for your business.
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Run:AI
Run:AI
Virtualization Software for AI Infrastructure. Gain visibility and control over AI workloads to increase GPU utilization. Run:AI has built the world’s first virtualization layer for deep learning training models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling full utilization of expensive GPU resources. Gain control over the allocation of expensive GPU resources. Run:AI’s scheduling mechanism enables IT to control, prioritize and align data science computing needs with business goals. Using Run:AI’s advanced monitoring tools, queueing mechanisms, and automatic preemption of jobs based on priorities, IT gains full control over GPU utilization. By creating a flexible ‘virtual pool’ of compute resources, IT leaders can visualize their full infrastructure capacity and utilization across sites, whether on premises or in the cloud. -
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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.
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DeepCube
DeepCube
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud. -
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AWS Inferentia
Amazon
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. -
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Amazon EC2 G5 Instances
Amazon
Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.Starting Price: $1.006 per hour -
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NetApp AIPod
NetApp
NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments. -
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OpenVINO
Intel
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). -
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Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
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AWS Neuron
Amazon Web Services
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). -
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Zebra by Mipsology
Mipsology
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. -
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Oblivus
Oblivus
Our infrastructure is equipped to meet your computing requirements, be it one or thousands of GPUs, or one vCPU to tens of thousands of vCPUs, we've got you covered. Our resources are readily available to cater to your needs, whenever you need them. Switching between GPU and CPU instances is a breeze with our platform. You have the flexibility to deploy, modify, and rescale your instances according to your needs, without any hassle. Outstanding machine learning performance without breaking the bank. The latest technology at a significantly lower cost. Cutting-edge GPUs are designed to meet the demands of your workloads. Gain access to computational resources that are tailored to suit the intricacies of your models. Leverage our infrastructure to perform large-scale inference and access necessary libraries with our OblivusAI OS. Unleash the full potential of your gaming experience by utilizing our robust infrastructure to play games in the settings of your choice.Starting Price: $0.29 per hour -
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GMI Cloud
GMI Cloud
Build your generative AI applications in minutes on GMI GPU Cloud. GMI Cloud is more than bare metal. Train, fine-tune, and infer state-of-the-art models. Our clusters are ready to go with scalable GPU containers and preconfigured popular ML frameworks. Get instant access to the latest GPUs for your AI workloads. Whether you need flexible on-demand GPUs or dedicated private cloud instances, we've got you covered. Maximize GPU resources with our turnkey Kubernetes software. Easily allocate, deploy, and monitor GPUs or nodes with our advanced orchestration tools. Customize and serve models to build AI applications using your data. GMI Cloud lets you deploy any GPU workload quickly and easily, so you can focus on running ML models, not managing infrastructure. Launch pre-configured environments and save time on building container images, installing software, downloading models, and configuring environment variables. Or use your own Docker image to fit your needs.Starting Price: $2.50 per hour -
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Amazon EC2 P4 Instances
Amazon
Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.Starting Price: $11.57 per hour -
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Stochastic
Stochastic
Enterprise-ready AI system that trains locally on your data, deploys on your cloud and scales to millions of users without an engineering team. Build customize and deploy your own chat-based AI. Finance chatbot. xFinance, a 13-billion parameter model fine-tuned on an open-source model using LoRA. Our goal was to show that it is possible to achieve impressive results in financial NLP tasks without breaking the bank. Personal AI assistant, your own AI to chat with your documents. Single or multiple documents, easy or complex questions, and much more. Effortless deep learning platform for enterprises, hardware efficient algorithms to speed up inference at a lower cost. Real-time logging and monitoring of resource utilization and cloud costs of deployed models. xTuring is an open-source AI personalization software. xTuring makes it easy to build and control LLMs by providing a simple interface to personalize LLMs to your own data and application. -
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Ori GPU Cloud
Ori
Launch GPU-accelerated instances highly configurable to your AI workload & budget. Reserve thousands of GPUs in a next-gen AI data center for training and inference at scale. The AI world is shifting to GPU clouds for building and launching groundbreaking models without the pain of managing infrastructure and scarcity of resources. AI-centric cloud providers outpace traditional hyperscalers on availability, compute costs and scaling GPU utilization to fit complex AI workloads. Ori houses a large pool of various GPU types tailored for different processing needs. This ensures a higher concentration of more powerful GPUs readily available for allocation compared to general-purpose clouds. Ori is able to offer more competitive pricing year-on-year, across on-demand instances or dedicated servers. When compared to per-hour or per-usage pricing of legacy clouds, our GPU compute costs are unequivocally cheaper to run large-scale AI workloads.Starting Price: $3.24 per month -
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Amazon EC2 Inf1 Instances
Amazon
Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.Starting Price: $0.228 per hour -
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Deci
Deci AI
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. -
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NVIDIA GPU-Optimized AMI
Amazon
The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'Starting Price: $3.06 per hour -
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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.
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NVIDIA TensorRT
NVIDIA
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.Starting Price: Free -
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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.Starting Price: Free
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Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
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CentML
CentML
CentML accelerates Machine Learning workloads by optimizing models to utilize hardware accelerators, like GPUs or TPUs, more efficiently and without affecting model accuracy. Our technology boosts training and inference speed, lowers compute costs, increases your AI-powered product margins, and boosts your engineering team's productivity. Software is no better than the team who built it. Our team is stacked with world-class machine learning and system researchers and engineers. Focus on your AI products and let our technology take care of optimum performance and lower cost for you. -
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Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
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Nendo
Nendo
Nendo is the AI audio tool suite that allows you to effortlessly develop & use audio apps that amplify efficiency & creativity across all aspects of audio production. Time-consuming issues with machine learning and audio processing code are a thing of the past. AI is a transformative leap for audio production, amplifying efficiency and creativity in industries where audio is key. But building custom AI Audio solutions and operating them at scale is challenging. Nendo cloud empowers developers and businesses to seamlessly deploy Nendo applications, utilize premium AI audio models through APIs, and efficiently manage workloads at scale. From batch processing, model training, and inference to library management, and beyond - Nendo cloud is your solution. -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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Determined AI
Determined AI
Distributed training without changing your model code, determined takes care of provisioning machines, networking, data loading, and fault tolerance. Our open source deep learning platform enables you to train models in hours and minutes, not days and weeks. Instead of arduous tasks like manual hyperparameter tuning, re-running faulty jobs, and worrying about hardware resources. Our distributed training implementation outperforms the industry standard, requires no code changes, and is fully integrated with our state-of-the-art training platform. With built-in experiment tracking and visualization, Determined records metrics automatically, makes your ML projects reproducible and allows your team to collaborate more easily. Your researchers will be able to build on the progress of their team and innovate in their domain, instead of fretting over errors and infrastructure. -
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EdgeCortix
EdgeCortix
Breaking the limits in AI processors and edge AI inference acceleration. Where AI inference acceleration needs it all, more TOPS, lower latency, better area and power efficiency, and scalability, EdgeCortix AI processor cores make it happen. General-purpose processing cores, CPUs, and GPUs, provide developers with flexibility for most applications. However, these general-purpose cores don’t match up well with workloads found in deep neural networks. EdgeCortix began with a mission in mind: redefining edge AI processing from the ground up. With EdgeCortix technology including a full-stack AI inference software development environment, run-time reconfigurable edge AI inference IP, and edge AI chips for boards and systems, designers can deploy near-cloud-level AI performance at the edge. Think about what that can do for these and other applications. Finding threats, raising situational awareness, and making vehicles smarter. -
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ONNX
ONNX
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. -
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DeepSpeed
Microsoft
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.Starting Price: Free -
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VESSL AI
VESSL AI
Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows. Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.Starting Price: $100 + compute/month -
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Nscale
Nscale
Nscale is the Hyperscaler engineered for AI, offering high-performance computing optimized for training, fine-tuning, and intensive workloads. From our data centers to our software stack, we are vertically integrated in Europe to provide unparalleled performance, efficiency, and sustainability. Access thousands of GPUs tailored to your requirements using our AI cloud platform. Reduce costs, grow revenue, and run your AI workloads more efficiently on a fully integrated platform. Whether you're using Nscale's built-in AI/ML tools or your own, our platform is designed to simplify the journey from development to production. The Nscale Marketplace offers users access to various AI/ML tools and resources, enabling efficient and scalable model development and deployment. Serverless allows seamless, scalable AI inference without the need to manage infrastructure. It automatically scales to meet demand, ensuring low latency and cost-effective inference for popular generative AI models. -
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SynapseAI
Habana Labs
Like our accelerator hardware, was purpose-designed to optimize deep learning performance, efficiency, and most importantly for developers, ease of use. With support for popular frameworks and models, the goal of SynapseAI is to facilitate ease and speed for developers, using the code and tools they use regularly and prefer. In essence, SynapseAI and its many tools and support are designed to meet deep learning developers where you are — enabling you to develop what and how you want. Habana-based deep learning processors, preserve software investments, and make it easy to build new models— for both training and deployment of the numerous and growing models defining deep learning, generative AI and large language models. -
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Amazon EC2 Trn1 Instances
Amazon
Amazon Elastic Compute Cloud (EC2) Trn1 instances, powered by AWS Trainium chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and latent diffusion models. Trn1 instances offer up to 50% cost-to-train savings over other comparable Amazon EC2 instances. You can use Trn1 instances to train 100B+ parameter DL and generative AI models across a broad set of applications, such as text summarization, code generation, question answering, image and video generation, recommendation, and fraud detection. The AWS Neuron SDK helps developers train models on AWS Trainium (and deploy models on the AWS Inferentia chips). It integrates natively with frameworks such as PyTorch and TensorFlow so that you can continue using your existing code and workflows to train models on Trn1 instances.Starting Price: $1.34 per hour -
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AWS Deep Learning AMIs
Amazon
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. -
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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.
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ThirdAI
ThirdAI
ThirdAI (pronunciation: /THərd ī/ Third eye) is a cutting-edge Artificial intelligence startup carving scalable and sustainable AI. ThirdAI accelerator builds hash-based processing algorithms for training and inference with neural networks. The technology is a result of 10 years of innovation in finding efficient (beyond tensor) mathematics for deep learning. Our algorithmic innovation has demonstrated how we can make Commodity x86 CPUs 15x or faster than most potent NVIDIA GPUs for training large neural networks. The demonstration has shaken the common knowledge prevailing in the AI community that specialized processors like GPUs are significantly superior to CPUs for training neural networks. Our innovation would not only benefit current AI training by shifting to lower-cost CPUs, but it should also allow the “unlocking” of AI training workloads on GPUs that were not previously feasible. -
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Prem AI
Prem Labs
An intuitive desktop application designed to effortlessly deploy and self-host open-source AI models without exposing sensitive data to third-party. Seamlessly implement machine learning models with the user-friendly interface of OpenAI's API. Bypass the complexities of inference optimizations. Prem's got you covered. Develop, test, and deploy your models in just minutes. Dive into our rich resources and learn how to make the most of Prem. Make payments with Bitcoin and Cryptocurrency. It's a permissionless infrastructure, designed for you. Your keys, your models, we ensure end-to-end encryption. -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery. -
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NVIDIA NGC
NVIDIA
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI. -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
44
fal.ai
fal.ai
fal is a serverless Python runtime that lets you scale your code in the cloud with no infra management. Build real-time AI applications with lightning-fast inference (under ~120ms). Check out some of the ready-to-use models, they have simple API endpoints ready for you to start your own AI-powered applications. Ship custom model endpoints with fine-grained control over idle timeout, max concurrency, and autoscaling. Use common models such as Stable Diffusion, Background Removal, ControlNet, and more as APIs. These models are kept warm for free. (Don't pay for cold starts) Join the discussion around our product and help shape the future of AI. Automatically scale up to hundreds of GPUs and scale down back to 0 GPUs when idle. Pay by the second only when your code is running. You can start using fal on any Python project by just importing fal and wrapping existing functions with the decorator.Starting Price: $0.00111 per second -
45
Segmind
Segmind
Segmind provides simplified access to large computing. You can use it to run your high-performance workloads such as Deep learning training or other complex processing jobs. Segmind offers zero-setup environments within minutes and lets your share access with your team members. Segmind's MLOps platform can also be used to manage deep learning projects end-to-end with integrated data storage and experiment tracking. ML engineers are not cloud engineers and cloud infrastructure management is a pain. So, we abstracted away all of it so that your ML team can focus on what they do best, and build models better and faster. Training ML/DL models take time and can get expensive quickly. But with Segmind, you can scale up your compute seamlessly while also reducing your costs by up to 70%, with our managed spot instances. ML managers today don't have a bird's eye view of ML development activities and cost.Starting Price: $5 -
46
Blaize AI Studio
Blaize
AI Studio delivers AI-driven, application end-to-end data operations (DataOps), development operations (DevOps), and Machine Learning operations (MLOps) tools. Our AI Software Platform reduces your dependency on critical resources like Data Scientists and Machine Learning (ML) engineers, reduces the time from development to deployment, and makes it easier to manage edge AI systems over the product’s lifetime. AI Studio is designed for deployment to edge inference accelerators, on-premises edge servers, systems, and AI-as-a-Service (AIaaS) for cloud-based applications. Reducing the time between data capture and AI deployment at the Edge with powerful data-labeling and annotation functions. Automated process leveraging AI knowledge base, MarketPlace and guided strategies, enabling Business Experts with AI expertise and solutions adds. -
47
Peltarion
Peltarion
The Peltarion Platform is a low-code deep learning platform that allows you to build commercially viable AI-powered solutions, at speed and at scale. The platform allows you to build, tweak, fine-tune and deploy deep learning models. It is end-to-end, and lets you do everything from uploading data to building models and putting them into production. The Peltarion Platform and its precursor have been used to solve problems for organizations like NASA, Tesla, Dell, and Harvard. Build your own AI models or use our pre-trained ones. Just drag & drop, even the cutting-edge ones! Own the whole development process from building, training, tweaking to deploying AI. All under one hood. Operationalize AI and drive business value, with the help of our platform. Our Faster AI course is created for users who have no prior knowledge of AI. After completing seven short modules, users will be able to design and tweak their own AI models on the Peltarion platform. -
48
IntelliHub
Spotflock
We work closely with businesses to find out what are the common issues preventing companies from realising benefits. We design to open up opportunities that were previously not viable using conventional approaches Corporations -big and small, require an AI platform with complete empowerment and ownership. Tackle data privacy and adopt to AI platforms at a sustainable cost. Enhance the efficiency of businesses and augment the work humans do. We apply AI to gain control over repetitive or dangerous tasks and bypass human intervention, thereby expediting tasks with creativity and empathy. Machine Learning helps to give predictive capabilities to applications with ease. You can build classification and regression models. It can also do clustering and visualize different clusters. It supports multiple ML libraries like Weka, Scikit-Learn, H2O and Tensorflow. It includes around 22 different algorithms for building classification, regression and clustering models. -
49
Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
50
Groq
Groq
Groq is on a mission to set the standard for GenAI inference speed, helping real-time AI applications come to life today. An LPU inference engine, with LPU standing for Language Processing Unit, is a new type of end-to-end processing unit system that provides the fastest inference for computationally intensive applications with a sequential component, such as AI language applications (LLMs). The LPU is designed to overcome the two LLM bottlenecks, compute density and memory bandwidth. An LPU has greater computing capacity than a GPU and CPU in regards to LLMs. This reduces the amount of time per word calculated, allowing sequences of text to be generated much faster. Additionally, eliminating external memory bottlenecks enables the LPU inference engine to deliver orders of magnitude better performance on LLMs compared to GPUs. Groq supports standard machine learning frameworks such as PyTorch, TensorFlow, and ONNX for inference.
