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Rafay Team

52.6% More Tokens, 1.53× Less GPU Time: Production Inference with Minima and Rafay

A single NVIDIA Blackwell GPU, an optimized Qwen3.6-27B worker from Minima, and Rafay Token Factory around it. The result was a governed, multi-tenant, token-metered service that delivered 52.6% more output than a strong FP8 baseline, with usage records reconciled at 99.97% for billing

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News

Rafay and Stealthium Partner to Bring Verifiable Security to Shared GPU Infrastructure

As more tenants share the same GPU infrastructure, operators need to know that isolation actually holds. Rafay and Stealthium are working together to combine enforcement across shared infrastructure with runtime visibility into what happens on each GPU.

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News

Rafay Platform Achieves NVIDIA-Certified Hypervisors Status

Rafay's VMaaS offering is now NVIDIA-Certified for HGX and NVL72 systems, giving operators secure multi-tenancy at near bare-metal GPU performance.

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News

Where AI Leaders Meet: Rafay’s Infrastructure Leadership Summit in Barcelona

The largest infrastructure build-out of our era is underway, and it is sorting the operators who simply own compute from the ones building lasting businesses on top of it. Rafay's AI Infrastructure Leadership Summit brings those operators into one room for two days on the journey from GPU to token: where margin actually comes from, how the best move from selling GPU hours to selling outcomes, and what it takes to build an AI business that lasts.

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News

Rafay and VotalAI Partner to Deploy AI Securely at Scale

Infrastructure security alone no longer covers the full AI interaction path. Rafay and VotalAI are partnering to secure enterprise AI at runtime, across prompts, agents, tools, and model responses.

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Product

More Than GPU Hours: What It Really Means to Monetize AI Infrastructure

GPU hours capture only a fraction of the value created by modern AI infrastructure. As telcos, governments, banks, healthcare systems, and regional providers build private and sovereign AI clouds, they need commercial models that reflect what customers actually consume and value. Rafay and Solvimon connect infrastructure-level metering with flexible billing to turn GPU capacity into governed, differentiated, and billable AI services.

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News

Securing inference without slowing it down: Rafay and LuminAI

Open-weight models are moving into production for cost, latency, and sovereignty, and securing them at the inference layer has become essential. Rafay and LuminAI integrate runtime protection directly into the token path, giving neoclouds, sovereign AI clouds, and enterprises security that runs in line with serving rather than bolted on around it, with serving performance intact.

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News

Rafay and Monetize360 Bring Revenue Operations to GPU Clouds

Rafay and Monetize360 help GPU cloud providers turn metered GPU and token usage into revenue-ready pricing, billing, invoicing, and customer financial workflows.

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News

Rafay and Cisco Partner To Power High-Performance AI Factories For Neoclouds and Sovereign AI Initiatives

Rafay is now an official Cisco Solutions Plus partner—neoclouds and Sovereign AI can procure the Rafay Platform directly from Cisco and launch revenue-ready AI Factories in weeks.

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Product

‍Rafay Launches AI Grid Orchestration Solution to Help Telcos Intelligently Deploy Distributed AI Infrastructure‍

Rafay, a member of the NVIDIA Inception program, brings infrastructure orchestration and workload automation to AI Grid architectures, enabling telcos and service providers to transform distributed GPU environments into a governed, self-service platform.

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From Infrastructure Validation to Market Validation: Rafay and NVIDIA DSX Air

Cloud service providers and enterprises that move fast, validate early, and get AI services in front of customers quickly will define the next era of AI infrastructure. NVIDIA DSX Air gives teams a pre-production simulation, to get a head start on the competition. Rafay makes that head start count by letting cloud service providers simulate business use cases and get customer feedback well before accelerated computing hardware is deployed.

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