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AI-ready

Validated configurations with NVIDIA GPUs: the practical starting point for AI in the company — sized, not improvised.

FOCUS · AI WITH THE RIGHT IRONValidated configurations, the right GPUs and the real question: how much iron is REALLY needed?
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YoctoIT material for clients and partners · Lenovo, ThinkSystem, ThinkAgile, TruScale and the other products mentioned are trademarks of Lenovo.
01 · What it is

Sistemi AI-ready, made clear.

Lenovo (among NVIDIA's biggest partners) offers validated AI configurations: from the ThinkSystems with L40S/H100/H200 for training and inference, to the Hybrid AI reference architectures, up to the dense racks with Neptune. The value for a company isn't the chip: it's the honest sizing — RAG and fine-tuning demand less iron than believed.

Validate
the server+GPU+network combinations tested: you start from recipes, not experiments
L40S→H200
the GPU for the task: the inference doesn't pay for the training's iron
OVX/HGX
the NVIDIA references in the Lenovo house: the scale ready when needed
Sistemi AI-ready
OFFICIAL LENOVO BRANDING · AI-READY
FOTO UFFICIALE LENOVO · THINKSYSTEM SR675i V3 CON GPU
OFFICIAL LENOVO PHOTO · THINKSYSTEM SR675i V3 WITH GPUs
02 · How to use it well

The things that make the difference.

The path

The AI use caseRAG, vision, fine-tuning
P workstations
GPU servers
Dense racks (Neptune)
try · produce · scale
The honest sizingthe iron on the real workload
NVIDIA AI Enterprisethe validated software stack
First the case, then the iron

The sizing that doesn't inflate

Model, context and requests/hour → the right GPU: often an L40S suffices where an H100 was feared.

The pilot on a workstation

You validate the use case on the small iron: the big investment only at proven value.

Shared GPUs

MIG and scheduling for several teams: the expensive resource exploited, not parked.

On-prem vs cloud, with numbers

The break-even calculated on YOUR volume: when the API pays, we say so.

03 · In depth

Hybrid AI Factory: from the PoC to production

Lenovo's AI is an engineered path: the GPU nodes (SR675 V3, SR780a with NVIDIA H200/B200, the HGX configurations) validated in the reference designs (with NVIDIA AI Enterprise and NIM), the AI Innovators program brings the vertical ISVs (vision, documents, manufacturing), the services (assessment, sizing, MLOps) accompany; the formula: start from the use case with ROI, size the iron on the real workload (inference vs fine-tuning), scale when the numbers add up.

  • Reference design — configurations validated with NVIDIA: the PoC without surprises
  • SR675/SR780a — 4-8 GPUs per node, air or liquid: the inference and the training
  • NVIDIA AI Enterprise — the supported software stack: NIM, frameworks, drivers
  • ISV verticali — the ready solutions for vision and documents: the time-to-value
  • Sizing sul carico — real tokens/s and batches: the right iron, not the biggest
  • Scalabilità — from the single node to the factory: you grow with the results
04 · Numbers and lifecycle

The numbers that matter.

4-8
typical GPUs per node
PoC
in weeks on the reference designs
ROI
the scaling criterion: the numbers before the iron
Neptune
the liquid ready when the density rises
In-house AI starts from the use case: assessment, sizing and stack — the first project we deliver, measurable.
05 · Use cases

Where it really pays off.

Corporate RAG

The knowledge queryable, on the in-house iron.

Industrial vision

The edge GPUs for in-line quality.

Data science teams

The power shared, governed.

AI isn't buying GPUs: it's sizing them. Validated iron + honest sizing = our approach.