Alternatives to Tinfoil

Google Cloud's Confidential Computing offers hardware-based Trusted Execution Environments (TEEs) that encrypt data while it is actively being used, thus completing the encryption process for data both at rest and in transit. This suite includes Confidential VMs, which utilize AMD SEV, SEV-SNP, Intel TDX, and NVIDIA confidential GPUs, alongside Confidential Space facilitating secure multi-party data sharing, Google Cloud Attestation, and split-trust encryption tools. Confidential VMs are designed to support workloads within Compute Engine and are applicable across various services such as Dataproc, Dataflow, GKE, and Vertex AI Workbench. The underlying architecture guarantees that memory is encrypted during runtime, isolates workloads from the host operating system and hypervisor, and includes attestation features that provide customers with proof of operation within a secure enclave. Use cases are diverse, spanning confidential analytics, federated learning in sectors like healthcare and finance, generative AI model deployment, and collaborative data sharing in supply chains. Ultimately, this innovative approach minimizes the trust boundary to only the guest application rather than the entire computing environment, enhancing overall security and privacy for sensitive workloads.

Azure Confidential Computing enhances the privacy and security of data by safeguarding it during processing, rather than merely when it is stored or transmitted. It achieves this by encrypting data in memory through hardware-based trusted execution environments, enabling computations to occur only after the cloud platform has authenticated the environment. This method effectively blocks access from cloud service providers, administrators, and other privileged users. Additionally, it facilitates scenarios like multi-party analytics, where various organizations can collaboratively use encrypted datasets for joint machine learning efforts without disclosing their respective data. Users maintain complete control over their data and code, dictating which hardware and software can access them, and they can transition existing workloads using familiar tools, SDKs, and cloud infrastructures. Ultimately, this approach not only fosters collaboration but also significantly bolsters trust in cloud computing environments.

Phala provides a confidential compute cloud that secures AI workloads using TEEs and hardware-level encryption to protect both models and data. The platform makes it possible to run sensitive AI tasks without exposing information to operators, operating systems, or external threats. With a library of ready-to-deploy confidential AI models—including options from OpenAI, Google, Meta, DeepSeek, and Qwen—teams can achieve private, high-performance inference instantly. Phala’s GPU TEE technology delivers nearly native compute speeds across H100, H200, and B200 chips while guaranteeing full isolation and verifiability. Developers can deploy workflows through Phala Cloud using simple Docker or Kubernetes setups, aided by automatic environment encryption and real-time attestation. Phala meets stringent enterprise requirements, offering SOC 2 Type II compliance, HIPAA-ready infrastructure, GDPR-aligned processing, and a 99.9% uptime SLA. Companies across finance, healthcare, legal AI, SaaS, and decentralized AI rely on Phala to enable use cases requiring absolute data confidentiality. With rapid adoption and strong performance, Phala delivers the secure foundation needed for trustworthy AI.

NVIDIA Confidential Computing safeguards data while it is actively being processed, ensuring the protection of AI models and workloads during execution by utilizing hardware-based trusted execution environments integrated within the NVIDIA Hopper and Blackwell architectures, as well as compatible platforms. This innovative solution allows businesses to implement AI training and inference seamlessly, whether on-site, in the cloud, or at edge locations, without requiring modifications to the model code, all while maintaining the confidentiality and integrity of both their data and models. Among its notable features are the zero-trust isolation that keeps workloads separate from the host operating system or hypervisor, device attestation that confirms only authorized NVIDIA hardware is executing the code, and comprehensive compatibility with shared or remote infrastructures, catering to ISVs, enterprises, and multi-tenant setups. By protecting sensitive AI models, inputs, weights, and inference processes, NVIDIA Confidential Computing facilitates the execution of high-performance AI applications without sacrificing security or efficiency. This capability empowers organizations to innovate confidently, knowing their proprietary information remains secure throughout the entire operational lifecycle.

SiliconFlow is an advanced AI infrastructure platform tailored for developers, providing a comprehensive and scalable environment for executing, optimizing, and deploying both language and multimodal models. With its impressive speed, minimal latency, and high throughput, it ensures swift and dependable inference across various open-source and commercial models while offering versatile options such as serverless endpoints, dedicated computing resources, or private cloud solutions. The platform boasts a wide array of features, including integrated inference capabilities, fine-tuning pipelines, and guaranteed GPU access, all facilitated through an OpenAI-compatible API that comes equipped with built-in monitoring, observability, and intelligent scaling to optimize costs. For tasks that rely on diffusion, SiliconFlow includes the open-source OneDiff acceleration library, and its BizyAir runtime is designed to efficiently handle scalable multimodal workloads. Built with enterprise-level stability in mind, it incorporates essential features such as BYOC (Bring Your Own Cloud), strong security measures, and real-time performance metrics, making it an ideal choice for organizations looking to harness the power of AI effectively. Furthermore, SiliconFlow's user-friendly interface ensures that developers can easily navigate and leverage its capabilities to enhance their projects.

Fortanix Confidential AI presents a comprehensive platform that allows data teams to handle sensitive datasets and deploy AI/ML models exclusively within secure computing environments, integrating managed infrastructure, software, and workflow orchestration to uphold privacy compliance across organizations. This service features on-demand infrastructure driven by the high-performance Intel Ice Lake third-generation scalable Xeon processors, enabling the execution of AI frameworks within Intel SGX and other enclave technologies while ensuring no external visibility. Moreover, it offers hardware-backed execution proofs and comprehensive audit logs to meet rigorous regulatory standards, safeguarding every aspect of the MLOps pipeline, from data ingestion through Amazon S3 connectors or local uploads to model training, inference, and fine-tuning, while also ensuring compatibility across a wide range of models. By leveraging this platform, organizations can significantly enhance their ability to manage sensitive information responsibly while advancing their AI initiatives.

Model Studio serves as Alibaba Cloud's comprehensive generative AI platform, empowering developers to create intelligent applications that are attuned to business needs by utilizing top-tier foundation models such as Qwen-Max, Qwen-Plus, Qwen-Turbo, the Qwen-2/3 series, visual-language models like Qwen-VL/Omni, and the video-centric Wan series. With this platform, users can easily tap into these advanced GenAI models through user-friendly OpenAI-compatible APIs or specialized SDKs, eliminating the need for any infrastructure setup. The platform encompasses a complete development workflow, allowing for experimentation with models in a dedicated playground, conducting both real-time and batch inferences, and fine-tuning using methods like SFT or LoRA. After fine-tuning, users can evaluate and compress their models, speed up deployment, and monitor performance—all within a secure, isolated Virtual Private Cloud (VPC) designed for enterprise-level security. Furthermore, one-click Retrieval-Augmented Generation (RAG) makes it easy to customize models by integrating specific business data into their outputs. The intuitive, template-based interfaces simplify prompt engineering and facilitate the design of applications, making the entire process more accessible for developers of varying skill levels. Overall, Model Studio empowers organizations to harness the full potential of generative AI efficiently and securely.

Kluster.ai is an AI cloud platform tailored for developers, enabling quick deployment, scaling, and fine-tuning of large language models (LLMs) with remarkable efficiency. Crafted by developers with a focus on developer needs, it features Adaptive Inference, a versatile service that dynamically adjusts to varying workload demands, guaranteeing optimal processing performance and reliable turnaround times. This Adaptive Inference service includes three unique processing modes: real-time inference for tasks requiring minimal latency, asynchronous inference for budget-friendly management of tasks with flexible timing, and batch inference for the streamlined processing of large volumes of data. It accommodates an array of innovative multimodal models for various applications such as chat, vision, and coding, featuring models like Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3. Additionally, Kluster.ai provides an OpenAI-compatible API, simplifying the integration of these advanced models into developers' applications, and thereby enhancing their overall capabilities. This platform ultimately empowers developers to harness the full potential of AI technologies in their projects.

Fireworks collaborates with top generative AI researchers to provide the most efficient models at unparalleled speeds. It has been independently assessed and recognized as the fastest among all inference providers. You can leverage powerful models specifically selected by Fireworks, as well as our specialized multi-modal and function-calling models developed in-house. As the second most utilized open-source model provider, Fireworks impressively generates over a million images each day. Our API, which is compatible with OpenAI, simplifies the process of starting your projects with Fireworks. We ensure dedicated deployments for your models, guaranteeing both uptime and swift performance. Fireworks takes pride in its compliance with HIPAA and SOC2 standards while also providing secure VPC and VPN connectivity. You can meet your requirements for data privacy, as you retain ownership of your data and models. With Fireworks, serverless models are seamlessly hosted, eliminating the need for hardware configuration or model deployment. In addition to its rapid performance, Fireworks.ai is committed to enhancing your experience in serving generative AI models effectively. Ultimately, Fireworks stands out as a reliable partner for innovative AI solutions.

Cosmian’s Data Protection Suite offers a robust and advanced cryptography solution designed to safeguard sensitive data and applications, whether they are actively used, stored, or transmitted through cloud and edge environments. This suite features Cosmian Covercrypt, a powerful hybrid encryption library that combines classical and post-quantum techniques, providing precise access control with traceability; Cosmian KMS, an open-source key management system that facilitates extensive client-side encryption dynamically; and Cosmian VM, a user-friendly, verifiable confidential virtual machine that ensures its own integrity through continuous cryptographic checks without interfering with existing operations. Additionally, the AI Runner known as “Cosmian AI” functions within the confidential VM, allowing for secure model training, querying, and fine-tuning without the need for programming skills. All components are designed for seamless integration via straightforward APIs and can be quickly deployed through marketplaces such as AWS, Azure, or Google Cloud, thus enabling organizations to establish zero-trust security frameworks efficiently. The suite’s innovative approach not only enhances data security but also streamlines operational processes for businesses across various sectors.

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.

Enhance and launch AI models using Simplismart's ultra-fast inference engine. Seamlessly connect with major cloud platforms like AWS, Azure, GCP, and others for straightforward, scalable, and budget-friendly deployment options. Easily import open-source models from widely-used online repositories or utilize your personalized custom model. You can opt to utilize your own cloud resources or allow Simplismart to manage your model hosting. With Simplismart, you can go beyond just deploying AI models; you have the capability to train, deploy, and monitor any machine learning model, achieving improved inference speeds while minimizing costs. Import any dataset for quick fine-tuning of both open-source and custom models. Efficiently conduct multiple training experiments in parallel to enhance your workflow, and deploy any model on our endpoints or within your own VPC or on-premises to experience superior performance at reduced costs. The process of streamlined and user-friendly deployment is now achievable. You can also track GPU usage and monitor all your node clusters from a single dashboard, enabling you to identify any resource limitations or model inefficiencies promptly. This comprehensive approach to AI model management ensures that you can maximize your operational efficiency and effectiveness.

nilGPT serves as a privacy-centric AI chat partner that prioritizes secure and anonymous engagement. The platform asserts that all interactions are governed by a principle of “data private by default,” where user inputs are fragmented and distributed across various nilDB nodes, while AI operations occur within secure enclaves, ensuring that data remains unexposed in a centralized manner. It presents a variety of tailored conversation modes, including wellness, personal assistant, and companion, to cater to diverse user needs. The service is designed to be a safe environment where individuals can express sensitive thoughts or personal matters without concerns about data retention or monitoring. Users can access it through both a web chat interface and a dedicated app, with the flexibility to either sign in or engage anonymously. According to the information available on its GitHub repository, nilGPT is constructed with “SecretLLM + SecretVaults” and is fully open source under the MIT license, promoting transparency and community collaboration. The focus on user privacy and customization makes nilGPT a distinctive choice in the landscape of AI chat companions.

Baseten is a cloud-native platform focused on delivering robust and scalable AI inference solutions for businesses requiring high reliability. It enables deployment of custom, open-source, and fine-tuned AI models with optimized performance across any cloud or on-premises infrastructure. The platform boasts ultra-low latency, high throughput, and automatic autoscaling capabilities tailored to generative AI tasks like transcription, text-to-speech, and image generation. Baseten’s inference stack includes advanced caching, custom kernels, and decoding techniques to maximize efficiency. Developers benefit from a smooth experience with integrated tooling and seamless workflows, supported by hands-on engineering assistance from the Baseten team. The platform supports hybrid deployments, enabling overflow between private and Baseten clouds for maximum performance. Baseten also emphasizes security, compliance, and operational excellence with 99.99% uptime guarantees. This makes it ideal for enterprises aiming to deploy mission-critical AI products at scale.

Maple AI serves as a privacy-centric, versatile AI assistant tailored for professionals and individuals who value confidentiality in their online communications. Constructed with robust end-to-end encryption, secure enclaves, and a commitment to open-source transparency, Maple guarantees that your discussions remain your own, safeguarded, and available at any time and place. Whether you are a therapist handling sensitive client details, a lawyer preparing confidential materials, or an entrepreneur brainstorming innovative ideas, Maple AI facilitates secure and effective productivity. It enables seamless synchronization across various devices, allowing users to transition smoothly from desktop to mobile, ensuring they can continue from where they last left off without hassle. Maple AI creates a uniform and secure experience on all platforms. Its features, including chat history search, AI-generated chat naming, and tailored chat organization, significantly boost user productivity. Additionally, Maple provides a user-friendly interface that makes navigating through its features both intuitive and efficient, catering to a diverse range of professional needs.

Privatemode offers an AI service similar to ChatGPT, distinguished by its commitment to user data privacy. By utilizing confidential computing techniques, Privatemode ensures that your data is encrypted right from your device, maintaining its protection throughout the AI processing stages. This guarantees that your sensitive information is safeguarded at every step. Key features include: Complete encryption: Thanks to confidential computing, your data is continuously encrypted, whether it is being transferred, stored, or processed in memory. Comprehensive attestation: The Privatemode application and proxy confirm the integrity of the service using cryptographic certificates issued by hardware, ensuring trustworthiness. Robust zero-trust architecture: The design of the Privatemode service actively prevents any unauthorized access to your data, including from Edgeless Systems. EU-based hosting: The Privatemode infrastructure is located in premier data centers within the European Union, with plans for additional locations in the near future. This commitment to privacy and security sets Privatemode apart in the landscape of AI services.

An AI system designed for businesses that facilitates local training on proprietary data and enables deployment on your chosen cloud infrastructure, capable of scaling to accommodate millions of users without requiring an engineering team. You can create, customize, and launch your own AI-driven chat interface, such as a finance chatbot named xFinance, which is based on a 13-billion parameter model fine-tuned on an open-source architecture using LoRA techniques. Our objective was to demonstrate that significant advancements in financial NLP tasks can be achieved affordably. Additionally, you can have a personal AI assistant that interacts with your documents, handling both straightforward and intricate queries across single or multiple documents. This platform offers a seamless deep learning experience for enterprises, featuring hardware-efficient algorithms that enhance inference speed while reducing costs. It also includes real-time monitoring and logging of resource use and cloud expenses associated with your deployed models. Furthermore, xTuring serves as open-source personalization software for AI, simplifying the process of building and managing large language models (LLMs) by offering an intuitive interface to tailor these models to your specific data and application needs, ultimately fostering greater efficiency and customization. With these innovative tools, companies can harness the power of AI to streamline their operations and enhance user engagement.

The Intel® Tiber™ AI Cloud serves as a robust platform tailored to efficiently scale artificial intelligence workloads through cutting-edge computing capabilities. Featuring specialized AI hardware, including the Intel Gaudi AI Processor and Max Series GPUs, it enhances the processes of model training, inference, and deployment. Aimed at enterprise-level applications, this cloud offering allows developers to create and refine models using well-known libraries such as PyTorch. Additionally, with a variety of deployment choices, secure private cloud options, and dedicated expert assistance, Intel Tiber™ guarantees smooth integration and rapid deployment while boosting model performance significantly. This comprehensive solution is ideal for organizations looking to harness the full potential of AI technologies.

Together AI offers a cloud platform purpose-built for developers creating AI-native applications, providing optimized GPU infrastructure for training, fine-tuning, and inference at unprecedented scale. Its environment is engineered to remain stable even as customers push workloads to trillions of tokens, ensuring seamless reliability in production. By continuously improving inference runtime performance and GPU utilization, Together AI delivers a cost-effective foundation for companies building frontier-level AI systems. The platform features a rich model library including open-source, specialized, and multimodal models for chat, image generation, video creation, and coding tasks. Developers can replace closed APIs effortlessly through OpenAI-compatible endpoints. Innovations such as ATLAS, FlashAttention, Flash Decoding, and Mixture of Agents highlight Together AI’s strong research contributions. Instant GPU clusters allow teams to scale from prototypes to distributed workloads in minutes. AI-native companies rely on Together AI to break performance barriers and accelerate time to market.

Open WebUI is a robust, user-friendly, and customizable AI platform that is self-hosted and capable of functioning entirely without an internet connection. It is compatible with various LLM runners, such as Ollama, alongside APIs that align with OpenAI standards, and features an integrated inference engine that supports Retrieval Augmented Generation (RAG), positioning it as a formidable choice for AI deployment. Notable aspects include an easy installation process through Docker or Kubernetes, smooth integration with OpenAI-compatible APIs, detailed permissions, and user group management to bolster security, as well as a design that adapts well to different devices and comprehensive support for Markdown and LaTeX. Furthermore, Open WebUI presents a Progressive Web App (PWA) option for mobile usage, granting users offline access and an experience akin to native applications. The platform also incorporates a Model Builder, empowering users to develop tailored models from base Ollama models directly within the system. With a community of over 156,000 users, Open WebUI serves as a flexible and secure solution for the deployment and administration of AI models, making it an excellent choice for both individuals and organizations seeking offline capabilities. Its continuous updates and feature enhancements only add to its appeal in the ever-evolving landscape of AI technology.

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

We offer fast and precise AI models optimized for deployment in production environments. Our inference API is designed for high availability, utilizing cutting-edge NVIDIA GPUs to ensure optimal performance. We have curated a selection of top open-source natural language processing (NLP) models from the community, making them readily available for your use. You have the flexibility to fine-tune your own models, including GPT-J, or upload your proprietary models for seamless deployment in production. From your user-friendly dashboard, you can easily upload or train/fine-tune AI models, allowing you to integrate them into production immediately without the hassle of managing deployment factors such as memory usage, availability, or scalability. Moreover, you can upload an unlimited number of models and deploy them as needed, ensuring that you can continuously innovate and adapt to your evolving requirements. This provides a robust framework for leveraging AI technologies in your projects.

Lamini empowers organizations to transform their proprietary data into advanced LLM capabilities, providing a platform that allows internal software teams to elevate their skills to match those of leading AI teams like OpenAI, all while maintaining the security of their existing systems. It ensures structured outputs accompanied by optimized JSON decoding, features a photographic memory enabled by retrieval-augmented fine-tuning, and enhances accuracy while significantly minimizing hallucinations. Additionally, it offers highly parallelized inference for processing large batches efficiently and supports parameter-efficient fine-tuning that scales to millions of production adapters. Uniquely, Lamini stands out as the sole provider that allows enterprises to safely and swiftly create and manage their own LLMs in any environment. The company harnesses cutting-edge technologies and research that contributed to the development of ChatGPT from GPT-3 and GitHub Copilot from Codex. Among these advancements are fine-tuning, reinforcement learning from human feedback (RLHF), retrieval-augmented training, data augmentation, and GPU optimization, which collectively enhance the capabilities of AI solutions. Consequently, Lamini positions itself as a crucial partner for businesses looking to innovate and gain a competitive edge in the AI landscape.

NetApp AIPod presents a holistic AI infrastructure solution aimed at simplifying the deployment and oversight of artificial intelligence workloads. By incorporating NVIDIA-validated turnkey solutions like the NVIDIA DGX BasePOD™ alongside NetApp's cloud-integrated all-flash storage, AIPod brings together analytics, training, and inference into one unified and scalable system. This integration allows organizations to efficiently execute AI workflows, encompassing everything from model training to fine-tuning and inference, while also prioritizing data management and security. With a preconfigured infrastructure tailored for AI operations, NetApp AIPod minimizes complexity, speeds up the path to insights, and ensures smooth integration in hybrid cloud settings. Furthermore, its design empowers businesses to leverage AI capabilities more effectively, ultimately enhancing their competitive edge in the market.

NVIDIA TensorRT is a comprehensive suite of APIs designed for efficient deep learning inference, which includes a runtime for inference and model optimization tools that ensure minimal latency and maximum throughput in production scenarios. Leveraging the CUDA parallel programming architecture, TensorRT enhances neural network models from all leading frameworks, adjusting them for reduced precision while maintaining high accuracy, and facilitating their deployment across a variety of platforms including hyperscale data centers, workstations, laptops, and edge devices. It utilizes advanced techniques like quantization, fusion of layers and tensors, and precise kernel tuning applicable to all NVIDIA GPU types, ranging from edge devices to powerful data centers. Additionally, the TensorRT ecosystem features TensorRT-LLM, an open-source library designed to accelerate and refine the inference capabilities of contemporary large language models on the NVIDIA AI platform, allowing developers to test and modify new LLMs efficiently through a user-friendly Python API. This innovative approach not only enhances performance but also encourages rapid experimentation and adaptation in the evolving landscape of AI applications.

NVIDIA DGX Cloud Serverless Inference provides a cutting-edge, serverless AI inference framework designed to expedite AI advancements through automatic scaling, efficient GPU resource management, multi-cloud adaptability, and effortless scalability. This solution enables users to reduce instances to zero during idle times, thereby optimizing resource use and lowering expenses. Importantly, there are no additional charges incurred for cold-boot startup durations, as the system is engineered to keep these times to a minimum. The service is driven by NVIDIA Cloud Functions (NVCF), which includes extensive observability capabilities, allowing users to integrate their choice of monitoring tools, such as Splunk, for detailed visibility into their AI operations. Furthermore, NVCF supports versatile deployment methods for NIM microservices, granting the ability to utilize custom containers, models, and Helm charts, thus catering to diverse deployment preferences and enhancing user flexibility. This combination of features positions NVIDIA DGX Cloud Serverless Inference as a powerful tool for organizations seeking to optimize their AI inference processes.

KServe is a robust model inference platform on Kubernetes that emphasizes high scalability and adherence to standards, making it ideal for trusted AI applications. This platform is tailored for scenarios requiring significant scalability and delivers a consistent and efficient inference protocol compatible with various machine learning frameworks. It supports contemporary serverless inference workloads, equipped with autoscaling features that can even scale to zero when utilizing GPU resources. Through the innovative ModelMesh architecture, KServe ensures exceptional scalability, optimized density packing, and smart routing capabilities. Moreover, it offers straightforward and modular deployment options for machine learning in production, encompassing prediction, pre/post-processing, monitoring, and explainability. Advanced deployment strategies, including canary rollouts, experimentation, ensembles, and transformers, can also be implemented. ModelMesh plays a crucial role by dynamically managing the loading and unloading of AI models in memory, achieving a balance between user responsiveness and the computational demands placed on resources. This flexibility allows organizations to adapt their ML serving strategies to meet changing needs efficiently.

Introducing an open-source AI model that can be fine-tuned, distilled, and deployed across various platforms. Our newest instruction-tuned model comes in three sizes: 8B, 70B, and 405B, giving you options to suit different needs. With our open ecosystem, you can expedite your development process using a diverse array of tailored product offerings designed to meet your specific requirements. You have the flexibility to select between real-time inference and batch inference services according to your project's demands. Additionally, you can download model weights to enhance cost efficiency per token while fine-tuning for your application. Improve performance further by utilizing synthetic data and seamlessly deploy your solutions on-premises or in the cloud. Take advantage of Llama system components and expand the model's capabilities through zero-shot tool usage and retrieval-augmented generation (RAG) to foster agentic behaviors. By utilizing 405B high-quality data, you can refine specialized models tailored to distinct use cases, ensuring optimal functionality for your applications. Ultimately, this empowers developers to create innovative solutions that are both efficient and effective.

Tensormesh serves as an innovative caching layer designed for inference tasks involving large language models, allowing organizations to capitalize on intermediate computations, significantly minimize GPU consumption, and enhance both time-to-first-token and overall latency. By capturing and repurposing essential key-value cache states that would typically be discarded after each inference, it eliminates unnecessary computational efforts and achieves “up to 10x faster inference,” all while substantially reducing the strain on GPUs. The platform is versatile, accommodating both public cloud and on-premises deployments, and offers comprehensive observability, enterprise-level control, as well as SDKs/APIs and dashboards for seamless integration into existing inference frameworks, boasting compatibility with inference engines like vLLM right out of the box. Tensormesh prioritizes high performance at scale, enabling sub-millisecond repeated queries, and fine-tunes every aspect of inference from caching to computation, ensuring that organizations can maximize efficiency and responsiveness in their applications. In an increasingly competitive landscape, such enhancements provide a critical edge for companies aiming to leverage advanced language models effectively.

Create your generative AI solutions in just a few minutes with GMI GPU Cloud. GMI Cloud goes beyond simple bare metal offerings by enabling you to train, fine-tune, and run cutting-edge models seamlessly. Our clusters come fully prepared with scalable GPU containers and widely-used ML frameworks, allowing for immediate access to the most advanced GPUs tailored for your AI tasks. Whether you seek flexible on-demand GPUs or dedicated private cloud setups, we have the perfect solution for you. Optimize your GPU utility with our ready-to-use Kubernetes software, which simplifies the process of allocating, deploying, and monitoring GPUs or nodes through sophisticated orchestration tools. You can customize and deploy models tailored to your data, enabling rapid development of AI applications. GMI Cloud empowers you to deploy any GPU workload swiftly and efficiently, allowing you to concentrate on executing ML models instead of handling infrastructure concerns. Launching pre-configured environments saves you valuable time by eliminating the need to build container images, install software, download models, and configure environment variables manually. Alternatively, you can utilize your own Docker image to cater to specific requirements, ensuring flexibility in your development process. With GMI Cloud, you'll find that the path to innovative AI applications is smoother and faster than ever before.

ModelArk is the central hub for ByteDance’s frontier AI models, offering a comprehensive suite that spans video generation, image editing, multimodal reasoning, and large language models. Users can explore high-performance tools like Seedance 1.0 for cinematic video creation, Seedream 3.0 for 2K image generation, and DeepSeek-V3.1 for deep reasoning with hybrid thinking modes. With 500,000 free inference tokens per LLM and 2 million free tokens for vision models, ModelArk lowers the barrier for innovation while ensuring flexible scalability. Pricing is straightforward and cost-effective, with transparent per-token billing that allows businesses to experiment and scale without financial surprises. The platform emphasizes security-first AI, featuring full-link encryption, sandbox isolation, and controlled, auditable access to safeguard sensitive enterprise data. Beyond raw model access, ModelArk includes PromptPilot for optimization, plug-in integration, knowledge bases, and agent tools to accelerate enterprise AI development. Its cloud GPU resource pools allow organizations to scale from a single endpoint to thousands of GPUs within minutes. Designed to empower growth, ModelArk combines technical innovation, operational trust, and enterprise scalability in one seamless ecosystem.

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.

The Intel® Distribution of OpenVINO™ toolkit serves as an open-source AI development resource that speeds up inference on various Intel hardware platforms. This toolkit is crafted to enhance AI workflows, enabling developers to implement refined deep learning models tailored for applications in computer vision, generative AI, and large language models (LLMs). Equipped with integrated model optimization tools, it guarantees elevated throughput and minimal latency while decreasing the model size without sacrificing accuracy. OpenVINO™ is an ideal choice for developers aiming to implement AI solutions in diverse settings, spanning from edge devices to cloud infrastructures, thereby assuring both scalability and peak performance across Intel architectures. Ultimately, its versatile design supports a wide range of AI applications, making it a valuable asset in modern AI development.

Synexa AI allows users to implement AI models effortlessly with just a single line of code, providing a straightforward, efficient, and reliable solution. It includes a range of features such as generating images and videos, restoring images, captioning them, fine-tuning models, and generating speech. Users can access more than 100 AI models ready for production, like FLUX Pro, Ideogram v2, and Hunyuan Video, with fresh models being added weekly and requiring no setup. The platform's optimized inference engine enhances performance on diffusion models by up to four times, enabling FLUX and other widely-used models to generate outputs in less than a second. Developers can quickly incorporate AI functionalities within minutes through user-friendly SDKs and detailed API documentation, compatible with Python, JavaScript, and REST API. Additionally, Synexa provides high-performance GPU infrastructure featuring A100s and H100s distributed across three continents, guaranteeing latency under 100ms through smart routing and ensuring a 99.9% uptime. This robust infrastructure allows businesses of all sizes to leverage powerful AI solutions without the burden of extensive technical overhead.

You can access models through the integrated Chat UI of the app or by utilizing a local server that is compatible with OpenAI. The minimum specifications required include either an M1, M2, or M3 Mac, or a Windows PC equipped with a processor that supports AVX2 instructions. Additionally, Linux support is currently in beta. A primary advantage of employing a local LLM is the emphasis on maintaining privacy, which is a core feature of LM Studio. This ensures that your information stays secure and confined to your personal device. Furthermore, you have the capability to operate LLMs that you import into LM Studio through an API server that runs on your local machine. Overall, this setup allows for a tailored and secure experience when working with language models.

Armet AI offers a robust GenAI platform designed for security through Confidential Computing, encapsulating every phase from data ingestion and vectorization to LLM inference and response management within hardware-enforced secure enclaves. Utilizing technologies like Intel SGX, TDX, TiberTrust Services, and NVIDIA GPUs, it ensures that data remains encrypted whether at rest, in transit, or during processing; this is complemented by AI Guardrails that automatically cleanse sensitive inputs, enforce security protocols, identify inaccuracies, and adhere to organizational standards. Additionally, it provides comprehensive Data & AI Governance through consistent role-based access controls, collaborative project frameworks, and centralized management of access rights. The platform’s End-to-End Data Security guarantees zero-trust encryption across all layers, including storage, transit, and processing. Furthermore, Holistic Compliance ensures alignment with regulations such as GDPR, the EU AI Act, and SOC 2, safeguarding sensitive information like PII, PCI, and PHI, ultimately reinforcing the integrity and confidentiality of data handling processes. By addressing these vital aspects, Armet AI empowers organizations to leverage AI capabilities while maintaining stringent security and compliance measures.

Exafunction enhances the efficiency of your deep learning inference tasks, achieving up to a tenfold increase in resource utilization and cost savings. This allows you to concentrate on developing your deep learning application rather than juggling cluster management and performance tuning. In many deep learning scenarios, limitations in CPU, I/O, and network capacities can hinder the optimal use of GPU resources. With Exafunction, GPU code is efficiently migrated to high-utilization remote resources, including cost-effective spot instances, while the core logic operates on a low-cost CPU instance. Proven in demanding applications such as large-scale autonomous vehicle simulations, Exafunction handles intricate custom models, guarantees numerical consistency, and effectively manages thousands of GPUs working simultaneously. It is compatible with leading deep learning frameworks and inference runtimes, ensuring that models and dependencies, including custom operators, are meticulously versioned, so you can trust that you're always obtaining accurate results. This comprehensive approach not only enhances performance but also simplifies the deployment process, allowing developers to focus on innovation instead of infrastructure.

Xilinx's AI development platform for inference on its hardware includes a suite of optimized intellectual property (IP), tools, libraries, models, and example designs, all crafted to maximize efficiency and user-friendliness. This platform unlocks the capabilities of AI acceleration on Xilinx’s FPGAs and ACAPs, accommodating popular frameworks and the latest deep learning models for a wide array of tasks. It features an extensive collection of pre-optimized models that can be readily deployed on Xilinx devices, allowing users to quickly identify the most suitable model and initiate re-training for specific applications. Additionally, it offers a robust open-source quantizer that facilitates the quantization, calibration, and fine-tuning of both pruned and unpruned models. Users can also take advantage of the AI profiler, which performs a detailed layer-by-layer analysis to identify and resolve performance bottlenecks. Furthermore, the AI library provides open-source APIs in high-level C++ and Python, ensuring maximum portability across various environments, from edge devices to the cloud. Lastly, the efficient and scalable IP cores can be tailored to accommodate a diverse range of application requirements, making this platform a versatile solution for developers.

FriendliAI serves as an advanced generative AI infrastructure platform that delivers rapid, efficient, and dependable inference solutions tailored for production settings. The platform is equipped with an array of tools and services aimed at refining the deployment and operation of large language models (LLMs) alongside various generative AI tasks on a large scale. Among its key features is Friendli Endpoints, which empowers users to create and implement custom generative AI models, thereby reducing GPU expenses and hastening AI inference processes. Additionally, it facilitates smooth integration with well-known open-source models available on the Hugging Face Hub, ensuring exceptionally fast and high-performance inference capabilities. FriendliAI incorporates state-of-the-art technologies, including Iteration Batching, the Friendli DNN Library, Friendli TCache, and Native Quantization, all of which lead to impressive cost reductions (ranging from 50% to 90%), a significant decrease in GPU demands (up to 6 times fewer GPUs), enhanced throughput (up to 10.7 times), and a marked decrease in latency (up to 6.2 times). With its innovative approach, FriendliAI positions itself as a key player in the evolving landscape of generative AI solutions.

Introducing a user-friendly desktop application that simplifies the deployment and self-hosting of open-source AI models while safeguarding your sensitive information from external parties. Effortlessly integrate machine learning models using the straightforward interface provided by OpenAI's API. Navigate the intricacies of inference optimizations with ease, as Prem is here to assist you. You can develop, test, and launch your models in a matter of minutes, maximizing efficiency. Explore our extensive resources to enhance your experience with Prem. Additionally, you can make transactions using Bitcoin and other cryptocurrencies. This infrastructure operates without restrictions, empowering you to take control. With complete ownership of your keys and models, we guarantee secure end-to-end encryption for your peace of mind, allowing you to focus on innovation.

Accelerate the building, training, and deployment of models at scale through a fully managed infrastructure that provides essential tools and streamlined workflows. Launch personalized AI and LLMs on any infrastructure in mere seconds, effortlessly scaling inference as required. Tackle your most intensive tasks with batch job scheduling, ensuring you only pay for what you use on a per-second basis. Reduce costs effectively by utilizing GPU resources, spot instances, and a built-in automatic failover mechanism. Simplify complex infrastructure configurations by deploying with just a single command using YAML. Adjust to demand by automatically increasing worker capacity during peak traffic periods and reducing it to zero when not in use. Release advanced models via persistent endpoints within a serverless architecture, maximizing resource efficiency. Keep a close eye on system performance and inference metrics in real-time, tracking aspects like worker numbers, GPU usage, latency, and throughput. Additionally, carry out A/B testing with ease by distributing traffic across various models for thorough evaluation, ensuring your deployments are continually optimized for performance.

The Qualcomm Cloud AI SDK serves as a robust software suite aimed at enhancing the performance of trained deep learning models for efficient inference on Qualcomm Cloud AI 100 accelerators. It accommodates a diverse array of AI frameworks like TensorFlow, PyTorch, and ONNX, which empowers developers to compile, optimize, and execute models with ease. Offering tools for onboarding, fine-tuning, and deploying models, the SDK streamlines the entire process from preparation to production rollout. In addition, it includes valuable resources such as model recipes, tutorials, and sample code to support developers in speeding up their AI projects. This ensures a seamless integration with existing infrastructures, promoting scalable and efficient AI inference solutions within cloud settings. By utilizing the Cloud AI SDK, developers are positioned to significantly boost the performance and effectiveness of their AI-driven applications, ultimately leading to more innovative solutions in the field.

Choose a model, and we will take care of the deployment, hosting, version control, and optimization, ultimately providing you with an inference endpoint for your use. This way, you can focus on your core tasks while we manage the technical details.

01.AI delivers an all-encompassing platform for deploying AI and machine learning models, streamlining the journey of training, launching, and overseeing these models on a large scale. The platform equips businesses with robust tools to weave AI seamlessly into their workflows while minimizing the need for extensive technical expertise. Covering the entire spectrum of AI implementation, 01.AI encompasses model training, fine-tuning, inference, and ongoing monitoring. By utilizing 01.AI's services, organizations can refine their AI processes, enabling their teams to prioritize improving model efficacy over managing infrastructure concerns. This versatile platform caters to a variety of sectors such as finance, healthcare, and manufacturing, providing scalable solutions that enhance decision-making abilities and automate intricate tasks. Moreover, the adaptability of 01.AI ensures that businesses of all sizes can leverage its capabilities to stay competitive in an increasingly AI-driven market.

Amazon SageMaker simplifies the process of deploying machine learning models for making predictions, also referred to as inference, ensuring optimal price-performance for a variety of applications. The service offers an extensive range of infrastructure and deployment options tailored to fulfill all your machine learning inference requirements. As a fully managed solution, it seamlessly integrates with MLOps tools, allowing you to efficiently scale your model deployments, minimize inference costs, manage models more effectively in a production environment, and alleviate operational challenges. Whether you require low latency (just a few milliseconds) and high throughput (capable of handling hundreds of thousands of requests per second) or longer-running inference for applications like natural language processing and computer vision, Amazon SageMaker caters to all your inference needs, making it a versatile choice for data-driven organizations. This comprehensive approach ensures that businesses can leverage machine learning without encountering significant technical hurdles.