Best AI Infrastructure Platforms in South America - Page 3

Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.

Amazon SageMaker enables the identification of various types of unprocessed data, including images, text documents, and videos, while also allowing for the addition of meaningful labels and the generation of synthetic data to develop high-quality training datasets for machine learning applications. The platform provides two distinct options, namely Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which grant users the capability to either leverage a professional workforce to oversee and execute data labeling workflows or independently manage their own labeling processes. For those seeking greater autonomy in crafting and handling their personal data labeling workflows, SageMaker Ground Truth serves as an effective solution. This service simplifies the data labeling process and offers flexibility by enabling the use of human annotators through Amazon Mechanical Turk, external vendors, or even your own in-house team, thereby accommodating various project needs and preferences. Ultimately, SageMaker's comprehensive approach to data annotation helps streamline the development of machine learning models, making it an invaluable tool for data scientists and organizations alike.

Banana emerged from recognizing a significant gap within the market. The demand for machine learning is soaring, yet the complexities involved in deploying models into production remain daunting and technical. Our focus at Banana is to create the essential machine learning infrastructure that supports the digital economy. By streamlining the deployment process, we make it as easy as copying and pasting an API to transition models into production. This approach allows businesses of all sizes to harness advanced models effectively. We are convinced that making machine learning accessible to everyone will play a pivotal role in driving global business growth. Viewing machine learning as the foremost technological gold rush of the 21st century, Banana is strategically positioned to supply the necessary tools and resources for success. We envision a future where companies can innovate and thrive without being hindered by technical barriers.

Achieve prices that are 3-5 times more competitive than conventional cloud services. FluidStack combines underutilized GPUs from data centers globally to provide unmatched economic advantages in the industry. With just one platform and API, you can deploy over 50,000 high-performance servers in mere seconds. Gain access to extensive A100 and H100 clusters equipped with InfiniBand in just a few days. Utilize FluidStack to train, fine-tune, and launch large language models on thousands of cost-effective GPUs in a matter of minutes. By connecting multiple data centers, FluidStack effectively disrupts monopolistic GPU pricing in the cloud. Experience computing speeds that are five times faster while enhancing cloud efficiency. Instantly tap into more than 47,000 idle servers, all with tier 4 uptime and security, through a user-friendly interface. You can train larger models, set up Kubernetes clusters, render tasks more quickly, and stream content without delays. The setup process requires only one click, allowing for custom image and API deployment in seconds. Additionally, our engineers are available around the clock through Slack, email, or phone, acting as a seamless extension of your team to ensure you receive the support you need. This level of accessibility and assistance can significantly streamline your operations.

Verda is a next-generation AI cloud designed for teams building, training, and deploying advanced machine learning models. It delivers powerful GPU infrastructure with no quotas, approvals, or long sales processes. Users can choose from GPU instances, instant multi-node clusters, or fully managed serverless inference. Verda’s Blackwell-powered GPU clusters offer exceptional performance, massive VRAM, and high-speed InfiniBand™ interconnects. The platform is optimized for productivity, allowing developers to deploy, hibernate, and scale resources instantly. Verda supports both short-term experimentation and long-running production workloads. Built-in security, GDPR compliance, and ISO27001 certification ensure enterprise readiness. All datacenters are powered entirely by renewable energy. World-class engineering support is available directly through the platform. Verda delivers a developer-first AI cloud built for speed, flexibility, and reliability.

All necessary infrastructure, computing resources, and software tools (such as Cuda and various frameworks) have been established for you to train and implement your preferred deep-learning models seamlessly. You can easily launch GPU or CPU instances right from your web browser or automate the process using our Python API for greater efficiency. This flexibility ensures that you can focus on model development without worrying about the underlying setup.

NVIDIA Brev is designed to streamline AI and ML development by delivering ready-to-use GPU environments hosted on popular cloud platforms. With Launchables, users can rapidly deploy preconfigured compute instances tailored to their project’s needs, including GPU capacity, container images, and essential files like notebooks or GitHub repositories. These Launchables can be customized, named, and generated with just a few clicks, then easily shared across social networks or directly with collaborators. The platform includes a variety of prebuilt Launchables that incorporate NVIDIA’s latest AI frameworks, microservices, and Blueprints, allowing developers to get started without delay. NVIDIA Brev also offers a virtual GPU sandbox, making it simple to set up CUDA-enabled environments, run Python scripts, and work within Jupyter notebooks right from a browser. Developers can monitor Launchable usage metrics and leverage CLI tools for fast code editing and SSH access. This flexible, easy-to-use platform accelerates the entire AI development lifecycle from experimentation to deployment. It empowers teams and startups to innovate faster by removing traditional infrastructure barriers.

Fal represents a serverless Python environment enabling effortless cloud scaling of your code without the need for infrastructure management. It allows developers to create real-time AI applications with incredibly fast inference times, typically around 120 milliseconds. Explore a variety of pre-built models that offer straightforward API endpoints, making it easy to launch your own AI-driven applications. You can also deploy custom model endpoints, allowing for precise control over factors such as idle timeout, maximum concurrency, and automatic scaling. Utilize widely-used models like Stable Diffusion and Background Removal through accessible APIs, all kept warm at no cost to you—meaning you won’t have to worry about the expense of cold starts. Engage in conversations about our product and contribute to the evolution of AI technology. The platform can automatically expand to utilize hundreds of GPUs and retract back to zero when not in use, ensuring you only pay for compute resources when your code is actively running. To get started with fal, simply import it into any Python project and wrap your existing functions with its convenient decorator, streamlining the development process for AI applications. This flexibility makes fal an excellent choice for both novice and experienced developers looking to harness the power of AI.

A robust platform optimized for training is equipped with NVIDIA® H100 Tensor Core GPUs, offering competitive pricing and personalized support. Designed to handle extensive machine learning workloads, it allows for efficient multihost training across thousands of H100 GPUs interconnected via the latest InfiniBand network, achieving speeds of up to 3.2Tb/s per host. Users benefit from significant cost savings, with at least a 50% reduction in GPU compute expenses compared to leading public cloud services*, and additional savings are available through GPU reservations and bulk purchases. To facilitate a smooth transition, we promise dedicated engineering support that guarantees effective platform integration while optimizing your infrastructure and deploying Kubernetes. Our fully managed Kubernetes service streamlines the deployment, scaling, and management of machine learning frameworks, enabling multi-node GPU training with ease. Additionally, our Marketplace features a variety of machine learning libraries, applications, frameworks, and tools designed to enhance your model training experience. New users can take advantage of a complimentary one-month trial period, ensuring they can explore the platform's capabilities effortlessly. This combination of performance and support makes it an ideal choice for organizations looking to elevate their machine learning initiatives.

We developed a containerization platform entirely in Rust, aiming to achieve the quickest cold-start times possible. It allows you to scale seamlessly from hundreds of GPUs down to zero within seconds, ensuring that you only pay for the resources you utilize. You can deploy functions to the cloud in mere seconds while accommodating custom container images and specific hardware needs. Forget about writing YAML; our system simplifies the process. Startups and researchers in academia are eligible for free compute credits up to $25,000 on Modal, which can be applied to GPU compute and access to sought-after GPU types. Modal continuously monitors CPU utilization based on the number of fractional physical cores, with each physical core corresponding to two vCPUs. Memory usage is also tracked in real-time. For both CPU and memory, you are billed only for the actual resources consumed, without any extra charges. This innovative approach not only streamlines deployment but also optimizes costs for users.

Deploy GPU-accelerated instances that can be finely tuned to suit your AI requirements and financial plan. Secure access to thousands of GPUs within a cutting-edge AI data center, ideal for extensive training and inference operations. The trend in the AI landscape is clearly leaning towards GPU cloud solutions, allowing for the creation and deployment of innovative models while alleviating the challenges associated with infrastructure management and resource limitations. AI-focused cloud providers significantly surpass conventional hyperscalers in terms of availability, cost efficiency, and the ability to scale GPU usage for intricate AI tasks. Ori boasts a diverse array of GPU types, each designed to meet specific processing demands, which leads to a greater availability of high-performance GPUs compared to standard cloud services. This competitive edge enables Ori to deliver increasingly attractive pricing each year, whether for pay-as-you-go instances or dedicated servers. In comparison to the hourly or usage-based rates of traditional cloud providers, our GPU computing expenses are demonstrably lower for running extensive AI operations. Additionally, this cost-effectiveness makes Ori a compelling choice for businesses seeking to optimize their AI initiatives.

Instill Core serves as a comprehensive AI infrastructure solution that effectively handles data, model, and pipeline orchestration, making the development of AI-centric applications more efficient. Users can easily access it through Instill Cloud or opt for self-hosting via the instill-core repository on GitHub. The features of Instill Core comprise: Instill VDP: A highly adaptable Versatile Data Pipeline (VDP) that addresses the complexities of ETL for unstructured data, enabling effective pipeline orchestration. Instill Model: An MLOps/LLMOps platform that guarantees smooth model serving, fine-tuning, and continuous monitoring to achieve peak performance with unstructured data ETL. Instill Artifact: A tool that streamlines data orchestration for a cohesive representation of unstructured data. With its ability to simplify the construction and oversight of intricate AI workflows, Instill Core proves to be essential for developers and data scientists who are harnessing the power of AI technologies. Consequently, it empowers users to innovate and implement AI solutions more effectively.

Featherless is a provider of AI models, granting subscribers access to an ever-growing collection of Hugging Face models. With the influx of hundreds of new models each day, specialized tools are essential to navigate this expanding landscape. Regardless of your specific application, Featherless enables you to discover and utilize top-notch AI models. Currently, we offer support for LLaMA-3-based models, such as LLaMA-3 and QWEN-2, though it's important to note that QWEN-2 models are limited to a context length of 16,000. We are also planning to broaden our list of supported architectures in the near future. Our commitment to progress ensures that we continually integrate new models as they are released on Hugging Face, and we aspire to automate this onboarding process to cover all publicly accessible models with suitable architecture. To promote equitable usage of individual accounts, concurrent requests are restricted based on the selected plan. Users can expect output delivery rates ranging from 10 to 40 tokens per second, influenced by the specific model and the size of the prompt, ensuring a tailored experience for every subscriber. As we expand, we remain dedicated to enhancing our platform's capabilities and offerings.

Introducing an advanced enterprise studio designed for AI developers to effectively train, validate, fine-tune, and deploy AI models. The IBM® watsonx.ai™ AI studio is an integral component of the IBM watsonx™ AI and data platform, which unifies innovative generative AI capabilities driven by foundation models alongside traditional machine learning techniques, creating a robust environment that covers the entire AI lifecycle. Users can adjust and direct models using their own enterprise data to fulfill specific requirements, benefiting from intuitive tools designed for constructing and optimizing effective prompts. With watsonx.ai, you can develop AI applications significantly faster and with less data than ever before. Key features of watsonx.ai include: comprehensive AI governance that empowers enterprises to enhance and amplify the use of AI with reliable data across various sectors, and versatile, multi-cloud deployment options that allow seamless integration and execution of AI workloads within your preferred hybrid-cloud architecture. This makes it easier than ever for businesses to harness the full potential of AI technology.

Qubrid AI stands out as a pioneering company in the realm of Artificial Intelligence (AI), dedicated to tackling intricate challenges across various sectors. Their comprehensive software suite features AI Hub, a centralized destination for AI models, along with AI Compute GPU Cloud and On-Prem Appliances, and the AI Data Connector. Users can develop both their own custom models and utilize industry-leading inference models, all facilitated through an intuitive and efficient interface. The platform allows for easy testing and refinement of models, followed by a smooth deployment process that enables users to harness the full potential of AI in their initiatives. With AI Hub, users can commence their AI journey, transitioning seamlessly from idea to execution on a robust platform. The cutting-edge AI Compute system maximizes efficiency by leveraging the capabilities of GPU Cloud and On-Prem Server Appliances, making it easier to innovate and execute next-generation AI solutions. The dedicated Qubrid team consists of AI developers, researchers, and partnered experts, all committed to continually enhancing this distinctive platform to propel advancements in scientific research and applications. Together, they aim to redefine the future of AI technology across multiple domains.

Substrate serves as the foundation for agentic AI, featuring sophisticated abstractions and high-performance elements, including optimized models, a vector database, a code interpreter, and a model router. It stands out as the sole compute engine crafted specifically to handle complex multi-step AI tasks. By merely describing your task and linking components, Substrate can execute it at remarkable speed. Your workload is assessed as a directed acyclic graph, which is then optimized; for instance, it consolidates nodes that are suitable for batch processing. The Substrate inference engine efficiently organizes your workflow graph, employing enhanced parallelism to simplify the process of integrating various inference APIs. Forget about asynchronous programming—just connect the nodes and allow Substrate to handle the parallelization of your workload seamlessly. Our robust infrastructure ensures that your entire workload operates within the same cluster, often utilizing a single machine, thereby eliminating delays caused by unnecessary data transfers and cross-region HTTP requests. This streamlined approach not only enhances efficiency but also significantly accelerates task execution times.

NetMind.AI is an innovative decentralized computing platform and AI ecosystem aimed at enhancing global AI development. It capitalizes on the untapped GPU resources available around the globe, making AI computing power affordable and accessible for individuals, businesses, and organizations of varying scales. The platform offers diverse services like GPU rentals, serverless inference, and a comprehensive AI ecosystem that includes data processing, model training, inference, and agent development. Users can take advantage of competitively priced GPU rentals and effortlessly deploy their models using on-demand serverless inference, along with accessing a broad range of open-source AI model APIs that deliver high-throughput and low-latency performance. Additionally, NetMind.AI allows contributors to integrate their idle GPUs into the network, earning NetMind Tokens (NMT) as a form of reward. These tokens are essential for facilitating transactions within the platform, enabling users to pay for various services, including training, fine-tuning, inference, and GPU rentals. Ultimately, NetMind.AI aims to democratize access to AI resources, fostering a vibrant community of contributors and users alike.

Civo is a cloud-native service provider focused on delivering fast, simple, and cost-effective cloud infrastructure for modern applications and AI workloads. The platform features managed Kubernetes clusters with rapid 90-second launch times, helping developers accelerate development cycles and scale with ease. Alongside Kubernetes, Civo offers compute instances, managed databases, object storage, load balancers, and high-performance cloud GPUs powered by NVIDIA A100, including environmentally friendly carbon-neutral options. Their pricing is predictable and pay-as-you-go, ensuring transparency and no surprises for businesses. Civo supports machine learning workloads with fully managed auto-scaling environments starting at $250 per month, eliminating the need for ML or Kubernetes expertise. The platform includes comprehensive dashboards and developer tools, backed by strong compliance certifications such as ISO27001 and SOC2. Civo also invests in community education through its Academy, meetups, and extensive documentation. With trusted partnerships and real-world case studies, Civo helps businesses innovate faster while controlling infrastructure costs.

The Trn1 instances of Amazon Elastic Compute Cloud (EC2), driven by AWS Trainium chips, are specifically designed to enhance the efficiency of deep learning training for generative AI models, such as large language models and latent diffusion models. These instances provide significant cost savings of up to 50% compared to other similar Amazon EC2 offerings. They are capable of facilitating the training of deep learning and generative AI models with over 100 billion parameters, applicable in various domains, including text summarization, code generation, question answering, image and video creation, recommendation systems, and fraud detection. Additionally, the AWS Neuron SDK supports developers in training their models on AWS Trainium and deploying them on the AWS Inferentia chips. With seamless integration into popular frameworks like PyTorch and TensorFlow, developers can leverage their current codebases and workflows for training on Trn1 instances, ensuring a smooth transition to optimized deep learning practices. Furthermore, this capability allows businesses to harness advanced AI technologies while maintaining cost-effectiveness and performance.

Amazon EC2 Inf1 instances are specifically designed to provide efficient, high-performance machine learning inference at a competitive cost. They offer an impressive throughput that is up to 2.3 times greater and a cost that is up to 70% lower per inference compared to other EC2 offerings. Equipped with up to 16 AWS Inferentia chips—custom ML inference accelerators developed by AWS—these instances also incorporate 2nd generation Intel Xeon Scalable processors and boast networking bandwidth of up to 100 Gbps, making them suitable for large-scale machine learning applications. Inf1 instances are particularly well-suited for a variety of applications, including search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers have the advantage of deploying their ML models on Inf1 instances through the AWS Neuron SDK, which is compatible with widely-used ML frameworks such as TensorFlow, PyTorch, and Apache MXNet, enabling a smooth transition with minimal adjustments to existing code. This makes Inf1 instances not only powerful but also user-friendly for developers looking to optimize their machine learning workloads. The combination of advanced hardware and software support makes them a compelling choice for enterprises aiming to enhance their AI capabilities.

Leverage our APIs to harness the power of AI, ensuring you only pay for what you utilize, eliminating any idle costs while benefiting from exceptional speed and scalability. Elevate your offerings by incorporating AI-driven image generation, which produces high-quality and distinctive visuals for your users. Utilize AI text generation to create engaging content, automate responses, or tailor experiences to individual preferences. By integrating real-time speech recognition into your products, you can significantly boost accessibility and productivity. The API also facilitates the creation of voiceovers, enhances accessibility features, and allows for the development of interactive experiences. Moreover, you can synchronize speech with facial movements to achieve lifelike animations and enhance video quality. Automate repetitive tasks while optimizing workflows to improve operational efficiency. Extract valuable insights from your data to make well-informed business decisions, ensuring you remain competitive in your industry. Finally, stay ahead of the curve with advanced AI, powered by a global network of state-of-the-art computers, which offers personalized recommendations that enhance customer satisfaction and engagement. This comprehensive approach can transform the way you interact with your audience and streamline your business processes.

Nscale is a specialized hyperscaler designed specifically for artificial intelligence, delivering high-performance computing that is fine-tuned for training, fine-tuning, and demanding workloads. Our vertically integrated approach in Europe spans from data centers to software solutions, ensuring unmatched performance, efficiency, and sustainability in all our offerings. Users can tap into thousands of customizable GPUs through our advanced AI cloud platform, enabling significant cost reductions and revenue growth while optimizing AI workload management. The platform is crafted to facilitate a smooth transition from development to production, whether employing Nscale's internal AI/ML tools or integrating your own. Users can also explore the Nscale Marketplace, which provides access to a wide array of AI/ML tools and resources that support effective and scalable model creation and deployment. Additionally, our serverless architecture allows for effortless and scalable AI inference, eliminating the hassle of infrastructure management. This system dynamically adjusts to demand, guaranteeing low latency and economical inference for leading generative AI models, ultimately enhancing user experience and operational efficiency. With Nscale, organizations can focus on innovation while we handle the complexities of AI infrastructure.

NeevCloud offers cutting-edge GPU cloud services powered by NVIDIA GPUs such as the H200, GB200 NVL72 and others. These GPUs offer unmatched performance in AI, HPC and data-intensive workloads. Flexible pricing and energy-efficient graphics cards allow you to scale dynamically, reducing costs while increasing output. NeevCloud is ideal for AI model training and scientific research. It also ensures seamless integration, global accessibility, and media production. NeevCloud GPU Cloud Solutions offer unparalleled speed, scalability and sustainability.

Humiris AI represents a cutting-edge infrastructure platform designed for artificial intelligence that empowers developers to create sophisticated applications through the integration of multiple Large Language Models (LLMs). By providing a multi-LLM routing and reasoning layer, it enables users to enhance their generative AI workflows within a versatile and scalable framework. The platform caters to a wide array of applications, such as developing chatbots, fine-tuning several LLMs at once, facilitating retrieval-augmented generation, constructing advanced reasoning agents, performing in-depth data analysis, and generating code. Its innovative data format is compatible with all foundational models, ensuring smooth integration and optimization processes. Users can easily begin by registering, creating a project, inputting their LLM provider API keys, and setting parameters to generate a customized mixed model that meets their distinct requirements. Additionally, it supports deployment on users' own infrastructure, which guarantees complete data sovereignty and adherence to both internal and external regulations, fostering a secure environment for innovation and development. This flexibility not only enhances user experience but also ensures that developers can leverage the full potential of AI technology.

Investigate the most recent advancements in optimized AI models, link AI agents to data using NVIDIA NeMo, and deploy solutions seamlessly with NVIDIA NIM microservices. NVIDIA NIM comprises user-friendly inference microservices that enable the implementation of foundation models across various cloud platforms or data centers, thereby maintaining data security while promoting efficient AI integration. Furthermore, NVIDIA AI offers access to the Deep Learning Institute (DLI), where individuals can receive technical training to develop valuable skills, gain practical experience, and acquire expert knowledge in AI, data science, and accelerated computing. AI models produce responses based on sophisticated algorithms and machine learning techniques; however, these outputs may sometimes be inaccurate, biased, harmful, or inappropriate. Engaging with this model comes with the understanding that you accept the associated risks of any potential harm stemming from its responses or outputs. As a precaution, refrain from uploading any sensitive information or personal data unless you have explicit permission, and be aware that your usage will be tracked for security monitoring. Remember, the evolving landscape of AI requires users to stay informed and vigilant about the implications of deploying such technologies.