> GPT‑4 fine‑tuning (256 GB dataset) 4 × HX6000 + 48 H100 48 µs (storage) 7.2 TB/s $0.12/GB Real‑time video analytics (1080p × 60 fps) 2 × thinksystem SR770 + 8 H100 + NVMe 8 TB 55 µs 5.1 TB/s $0.10/GB Genomics variant calling (10 TB) 3 × ThinkSystem DE5000 63 µs 6.8 TB/s $0.09/GB

Based on Lenovo’s internal TCO model, Q4 2025.

.Lenovo’s AI‑Optimized Storage Architecture

Key hardware components

• ThinkSystem DE Series NVMe SSDs – Double‑layer NAND with 4 TB/drive capacity, delivering up to 6 GB/s sequential read and < 60 µs latency.
• ThinkSystem SR770 servers – dual‑socket Intel Xeon Scalable 4 ghz CPUs paired with up to 8 × NVIDIA H100 Tensor Core GPUs.
• FPGA‑accelerated data path – Lenovo’s proprietary SmartNICs offload compression, encryption, and AI data pre‑processing directly on the storage fabric.

Software stack

• Lenovo AI Data Fabric (ADF) – Unified storage pool that auto‑tags AI datasets, enables tiered placement, and exposes a POSIX‑compatible namespace for TensorFlow, PyTorch, and JAX.
• lenovo Intelligent Orchestration (LIO) – Kubernetes‑native controller that provisions GPU‑ready pods with storage QoS policies (latency‑critical vs. bulk‑ingest).
• Integrated OpenShift‑AI – Pre‑bundled operators for model training, feature store, and MLOps pipelines, reducing IaC overhead by 30 %.

Hyper‑Converged Infrastructure (HCI) for Enterprise AI

Lenovo ThinkAgile HX Series

• HX6000 – Combines compute, memory, and AI‑optimized storage in a single 2U chassis; up to 1.5 PB raw capacity and 12 × H100 GPUs.
• Distributed‑Cache Layer – Intel Optane Persistent Memory (PMEM) caches hot training tensors, cutting repeat‑read latency by 45 %.

AI‑centric integration

• Native support for Red Hat OpenShift Data Foundation – Enables seamless data federation between on‑premise HX nodes and public‑cloud object stores (AWS S3, Azure Blob).
• Accelerated model serving – Integrated NVIDIA Triton inference Server auto‑scales inference pods across the HCI fabric, achieving 4 ×  higher QPS compared with traditional VM‑based deployment.

Performance Benchmarks

Based on Lenovo’s internal TCO model, Q4 2025.

Benefits for Enterprise AI Workloads

• Scalable AI fabric – Linear performance growth up to 200 nodes without storage bottlenecks.
• Reduced total cost of ownership – Consolidation of compute and storage cuts rack footprint by 35 % and power consumption by 28 %.
• AI‑ready data protection – end‑to‑end encryption with hardware‑based key management, plus AI‑aware snapshotting that captures model checkpoints without pausing training.
• Hybrid‑cloud adaptability – Consistent API across on‑prem and public clouds simplifies workloads that span edge devices and central data centers.

Practical Deployment Tips

1. Capacity Planning
• estimate AI data velocity (GB/s) rather than raw storage size; over‑provision the NVMe tier by 20 % to accommodate burst training phases.
• Use Lenovo ADF’s “predictive tiering” to auto‑migrate cold tensors to SATA SSDs after 30 days of inactivity.

2. Network Architecture
• Deploy 200 GbE RoCE v2 fabric to saturate GPU‑to‑storage traffic; enable DCBX for lossless Ethernet on all HX nodes.
• Leverage Lenovo’s Active‑Optical Cables (AOC) for intra‑rack connectivity to cut latency below 150 ns.

3. Kubernetes Configuration
• Set storageClass lenovo‑ai‑fast‑nvme with volumeBindingMode: Immediate for latency‑critical pods.
• Enable node‑affinity rules to keep GPU‑intensive pods on HX nodes that host the corresponding data locality group.

4. Monitoring & Optimization
• Integrate Lenovo XClarity Insights with Prometheus; watch the storage_latency_seconds and gpu_memory_utilization metrics for auto‑scaling triggers.
• Run Lenovo’s “AI Workload Optimizer” tool quarterly; it recommends rebalancing data caches based on recent training patterns.

Real‑World Implementations

Case Study 1 – global Financial Institution

• Challenge: Real‑time fraud detection required sub‑100 µs access to transaction logs while training reinforcement‑learning models on terabytes of ancient data.
• Solution: Deployed a 12‑node thinkagile HX6000 cluster with 64 H100 GPUs and 2 PB AI‑optimized NVMe. Integrated Lenovo ADF with their Spark‑AI pipeline.
• Outcome: Fraud detection latency dropped from 210 ms to 42 ms, model training time reduced by 58 %, and the consolidated infrastructure saved $3.2 M annually in rack and power costs.

Case Study 2 – Tier‑1 Telecom Operator

• Challenge: Edge‑AI for 5G network slicing demanded high‑throughput inference at 10 km distance from the core data center.
• Solution: Rolled out two mini‑HX6000 pods at regional POPs, each with 4 H100 GPUs and local NVMe cache, synchronized with central AI‑fabric via lenovo’s SD‑WAN overlay.
• Outcome: Inference throughput increased to 250 k requests/second per site,with end‑to‑end latency of 73 µs,enabling the operator to launch dynamic slice allocation for 5G‑Advanced services.

future Roadmap & Ecosystem Alignment

• 2026 Q1: Declaration of Lenovo AI‑Ready Fabric v2, adding support for Habana Gaudi 3 accelerators and SPARC‑based storage controllers.
• Partner integrations – Ongoing collaborations with Microsoft Azure Arc, Google Anthos, and Red Hat OpenShift to expose Lenovo’s AI‑optimized storage as a native CSP service.
• Edge‑first AI – Roadmap includes lightweight ThinkAgile HX‑Edge appliances (1 U, 32 TB NVMe, 2 × H100) for on‑premises AI inference in factories and smart‑city deployments.

All specifications and performance figures reflect lenovo’s official product data sheets (released January 2025) and third‑party benchmark results from Gartner 2025 “AI‑Ready Infrastructure” report.