Nutanix заявляет, что к концу года добавит графические процессоры AMD в свою платформу вывода искусственного интеллекта

Что случилось

CRN reports that Nutanix CEO Rajiv Ramaswami said the company is expanding its hybrid-cloud platform to support AI inference on AMD GPUs as well as Nvidia GPUs. Ramaswami said Nutanix expects its first AMD-based solutions by the end of the calendar year and described the work as a joint development effort supported by AMD’s $250 million investment in Nutanix.

CRN reports that Ramaswami described Nutanix as having moved beyond its origins in hyperconverged infrastructure toward a broader software platform for virtual machines, containers, public clouds, external storage and AI applications. According to the report, Nutanix’s software can run on bare-metal infrastructure provided by AWS, Microsoft Azure and Google Cloud, as well as on GPU-focused neoclouds. The company does not sell hardware as an inventory-based business, although customers can choose a Supermicro appliance or servers from vendors such as Dell, HPE, Lenovo and Cisco.

The AI-specific development in the report is Nutanix’s plan to extend its inference stack from Nvidia GPUs to AMD GPUs. Ramaswami told CRN that Nutanix currently runs its software stack on Nvidia hardware and expects its first AMD solutions by the end of the calendar year. He said AMD’s $250 million investment in Nutanix supports joint development, early access to AMD GPUs and joint marketing. He explicitly declined to say that Nutanix receives first access, saying only that the company gets early access.

Ramaswami said Nutanix’s AI platform is intended to provide inference endpoints that can serve open models selected by customers, including large language models and smaller language models. CRN reports that he also described a gateway between applications and the models they use. The gateway is meant to provide visibility into costs and access, apply usage controls and help determine when an application should use a frontier model or a less expensive open model. The source does not identify specific AMD GPU models, benchmark results, customer deployments or pricing for these capabilities.

The interview also placed the GPU work within a wider effort to make Nutanix useful when customers cannot easily obtain new servers or storage. CRN reports that external-storage support remains a small but fast-growing share of deployments. Ramaswami cited support for Dell PowerFlex, Dell PowerStore and Everpure, with NetApp support described as coming online. He said the approach can allow some customers to use existing servers and storage arrays rather than purchase new hardware, although the report provides no independent measurement of how often this occurs or how much money customers save.

Подробности об источнике: crn.com ↗

Почему это важно

The reported expansion would give enterprise customers another hardware option for running AI inference, while Nutanix positions its software as a layer that can operate across on-premises data centers, public clouds and GPU-focused neoclouds. The practical value will depend on compatibility, performance, availability and cost, none of which CRN independently tested in the report.

Enterprise AI deployments increasingly involve a choice between using an external provider’s frontier model and operating inference infrastructure directly. CRN reports that Nutanix is trying to serve both paths: customers can consume models from providers such as OpenAI or Anthropic, or run open models through infrastructure managed by Nutanix. A platform that connects applications, models, GPUs and governance controls could simplify operations for organizations managing a mixture of cloud and on-premises resources.

AMD support could also matter because it gives customers an additional accelerator supplier. Nvidia remains the market leader, according to Ramaswami’s characterization quoted by CRN, but enterprises may seek alternatives because of price, availability, procurement constraints or strategic preference. The report supports the existence of Nutanix’s planned AMD integration and AMD’s investment; it does not establish that AMD hardware will match Nvidia on performance, software support, availability or total cost of ownership.

The emphasis on inference, rather than model training, is consequential for businesses that want to operate established models in production. Inference workloads can run continuously and may involve sensitive enterprise data, usage controls and recurring token costs. Nutanix’s stated gateway approach addresses those operational concerns in principle, including visibility into who accesses models and how much usage costs. However, CRN does not independently verify the gateway’s capabilities, security controls, latency, supported models or results in production environments.

The reported external-storage strategy broadens the infrastructure question beyond GPUs. If customers can place Nutanix software on existing servers and connect supported storage arrays, they may be able to modernize software without replacing every hardware component. That could be useful during component shortages, but it also creates certification, support and integration requirements. The source contains Ramaswami’s account of the strategy and cited platforms, not independent testing of reliability, deployment complexity or customer outcomes.

Что посмотреть дальше

The key milestones are whether Nutanix delivers the promised AMD solutions on schedule, which AMD accelerators and server configurations are supported, and whether customers can obtain measurable cost or capacity benefits. It is also worth watching how Nutanix’s inference gateway, external-storage support and channel strategy develop beyond the claims described in the interview.

The most immediate test is delivery. Ramaswami told CRN that Nutanix expects its first AMD GPU solutions by the end of the calendar year, but the report does not specify a launch date, supported accelerator family, geographic availability or general-availability status. Follow-up reporting should establish whether the first release is broadly purchasable, limited to selected customers or primarily a technical integration.

Технические детали определят, повлияет ли объявление на решения о покупке. Важные неизвестные включают пропускную способность вывода, задержку, объем памяти, требования к электропитанию, поддерживаемые платформы, форматы моделей, сертификацию серверов и относительную стоимость развертываний AMD и Nvidia. Источник не предоставляет сравнительных тестов, независимой проверки или отзывов клиентов для вывода Nutanix на базе AMD, поэтому эти вопросы остаются открытыми.

Программные средства управления Nutanix также заслуживают пристального внимания. Компания заявляет, что ее шлюз может отслеживать затраты, управлять доступом и использованием, а также направлять приложения к различным моделям. Пользователям следует искать документацию, показывающую, какие данные регистрируются, как применяются разрешения, могут ли администраторы устанавливать политику расходов или моделировать, а также как система обрабатывает сбои или неточные выходные данные модели. Ни одна из этих подробностей реализации не представлена ​​в отчете CRN.

Наконец, стратегия канала может определять, насколько широко будет принята платформа. Рамасвами сказал, что партнеры, как ожидается, выйдут за рамки продаж HCI и займутся миграцией, контейнерами, облачными услугами и услугами искусственного интеллекта, в то время как Nutanix планирует добавить больше платформ внешнего хранения и продолжить разработку своих предложений Kubernetes и AI. Практический вопрос заключается в том, получают ли партнеры достаточное обучение, сертификацию и стимулы для реализации этих проектов, и могут ли клиенты получить поддержку в смешанных аппаратных, облачных и модельных средах. CRN сообщает о приоритетах руководства, но не подтверждает независимо результаты привлечения партнеров или их внедрения.

Сопутствующие руководства и викторины