The great race of Artificial Intelligence (AI) in 2026 is no longer just about who trains the largest model, but about who manages to deploy pre-trained models into real-world applications: in stores, factories, hospitals, customer service centers, or critical infrastructure. With this idea as the backdrop, Lenovo used its Tech World @ CES 2026 event, held at Sphere (Las Vegas), to introduce a new family of servers and services specifically designed for inference — the stage where a model analyzes “new” data and makes decisions in real time.
This announcement comes at a pivotal moment for businesses: after the initial “shock” of generative AI and the craze for training and tuning models, the real challenge has shifted to everyday use. The key question now dominating tech committees is no longer “Can we do it?”, but “Can we do it reliably, with low latency, controlling costs, and avoiding turning it into an endless project?” Lenovo aims to answer this with a package that combines hardware, software, and services under its Hybrid AI Advantage umbrella and the Hybrid AI Factory proposal, featuring “pre-validated” deployments to shorten timelines and reduce risks.
Inference: From Theory to Tangible Returns
Lenovo defines inference as a plot twist: moving from training large language models to leveraging already trained models to analyze unseen data and decide instantly. It’s the shift from lab experiments to real business impact. According to the company’s approach, this is also where AI investments start delivering tangible returns: operational automation, real-time analytics, fraud detection, immediate recommendations, internal assistants, process agents, or clinical support in critical environments.
The core message is clear: AI cannot live only in the cloud. To generate value, it must run where the data resides (cloud, data center, and edge) and with the right infrastructure to avoid delays caused by latency, power bottlenecks, or deployment complexity. Lenovo cites an estimate from Futurum indicating why the market is moving in this direction: global inference infrastructure is projected to grow from $5 billion in 2024 to $48.8 billion in 2030, with a CAGR of 46.3%.
Three Servers for Three Scenarios: From Data Center to Rugged Edge
The core of the announcement is a range of “refined” inference servers, designed at different scales and for various objectives:
- Lenovo ThinkSystem SR675i: positioned as the “heavyweight” option for running full models with high scalability, suited for large workloads in sectors like manufacturing, critical healthcare, or finance. Specialized media describe it as a high-end system tailored for large-scale inference and accelerated simulation, featuring AMD EPYC platforms and NVIDIA GPUs as configured.
- Lenovo ThinkSystem SR650i: designed to deploy dense GPU inference within existing data centers, focusing on ease of installation and scaling without redesigning the entire space.
- Lenovo ThinkEdge SE455i: emphasizing “AI where the action is.” This compact server targets retail, telecommunications, and industry, engineered to bring inference to the edge with ultra-low latency and environmental resilience, operating within a range of approximately -5°C to 55°C.
At the same time, Lenovo reinforces two classic arguments that are gaining renewed importance today: power and financing. On one hand, the company links these systems to its Neptune cooling technology (air and liquid) as a response to energy bottlenecks that arise from densifying compute. On the other, it bolsters its proposal with TruScale, a pay-as-you-go model, enabling companies to grow without a sudden CAPEX spike often associated with AI projects.
“Pre-validated” for Speed: Nutanix, Red Hat, and Ubuntu Pro
Lenovo doesn’t just sell hardware. The announcement emphasizes that hardware forms the foundation of a modular architecture, Lenovo Hybrid AI Factory, which aims to provide a more direct path to deployment-ready solutions. Within this framework, the company highlights three hybrid inference platforms:
- ThinkAgile HX with Nutanix AI: geared toward centralized shared inference, promising to maximize GPU utilization, improve performance, and scale in a virtualized environment.
- Hybrid AI Inference with Red Hat AI: positioned as a robust enterprise platform for deployments requiring flexibility, security, and future growth, especially in AI agent scenarios.
- Hybrid AI Inference with Canonical Ubuntu Pro: designed as an affordable entry point for rapid experimentation and deployment with essential security, leveraging the scalability of the SR650i.
This approach reflects a practical insight: many companies hesitate not out of disinterest, but due to concerns that deployment will turn into a puzzle of dependencies, integrations, and internal policies difficult to manage.
Services to Keep AI Running Beyond the Pilot
To complete the picture, Lenovo introduces Hybrid AI Factory Services for inference: consulting, deployment, and managed services to establish high-performance environments tailored to sector-specific workloads. Key points include: “out-of-the-box” performance, ongoing support (including Premier Support), and the flexibility of TruScale to evolve AI operations.
It’s clear from the market perspective: in 2026, the value isn’t just about “having AI,” but about operating it: monitoring costs, ensuring availability, updating models, governing data, maintaining security, and preventing system degradation as demand grows.
Sphere as a Showcase: AI for Immersive Experiences
The event took place at Sphere, and Lenovo used it to demonstrate real-world cases: as a technology partner of Sphere Studios, the company claims its processing power contributes to creating immersive content within the venue, utilizing hundreds of ThinkSystem SR655 V3, AMD EPYC processors, and NVIDIA accelerated computing in production workflows.
Fundamentally, it’s the same message for entertainment: when experiences depend on data, rendering, and low latency, infrastructure becomes part of the product.
Frequently Asked Questions (FAQ)
What’s the difference between training a model and doing inference in production?
Training creates or tunes the model; inference uses it to analyze new data and make decisions in real time. This is where AI becomes an operational service—and where returns often materialize.
When does it make sense to deploy inference at the edge instead of only in the cloud?
When latency matters (retail, industry, telecom), when data is generated locally, or when resilience and uptime are required despite limited connectivity.
What benefits does a “pre-validated” platform like Hybrid AI Factory offer over assembling components separately?
It reduces compatibility risks, accelerates time-to-market, and simplifies deployment—especially in environments where security, virtualization, and operational consistency matter more than “experimental” setups.
Why are cooling (Neptune) and pay-per-use (TruScale) important in inference projects?
Because large-scale inference can increase compute density and power consumption; cooling prevents thermal bottlenecks, and pay-per-use helps dimension and expand without over-investing upfront.
via: news.lenovo