8 Infrastructure teams have been retrofitting storage designed for enterprise file and backup to support AI workloads. That approach breaks at scale. AI compute infrastructure demands a fundamentally different architecture — one built around high-throughput GPU feeding, disaggregated storage, and workload-aligned tiers. Getting those components right from the start determines whether your AI platform scales or stalls. This guide covers the components of AI compute infrastructure, the design principles that separate functional deployments from ones that hit walls at petabyte scale, and how modern storage platforms integrate with the full compute stack. What is AI compute infrastructure? AI compute infrastructure is the combination of hardware, networking, storage, and orchestration software that supports machine learning training, inference, and data preparation workloads. It differs from general-purpose compute infrastructure in three ways: workloads are GPU-centric rather than CPU-centric; data volumes grow much faster than traditional enterprise applications; and I/O patterns are bursty, parallel, and latency-sensitive at every stage of the AI data lifecycle. A training cluster pulling a 100 TB dataset across thousands of GPU hours has different infrastructure requirements than a transactional database or a file server. The memory bandwidth, storage throughput, and network fabric have to be co-designed. Bolting AI workloads onto legacy infrastructure produces the same result every time: GPU underutilization, training bottlenecks, and engineers spending cycles on infrastructure instead of models. Key components of AI compute infrastructure This infrastructure is built from five layers. Each layer has specific performance requirements, and the layers have to be designed together — not assembled from whatever is available. Component Role Key metric GPU clusters Parallel matrix computation for training and inference GPU utilization %, memory bandwidth High-speed networking / RDMA Low-latency data movement between compute and storage Latency (µs), bandwidth (Tb/s) Storage tiers Feed training data, checkpoints, embeddings, inference cache Throughput (GB/s), IOPS, capacity Orchestration (Kubernetes / Slurm) Schedule GPU jobs, manage resource contention Job queue depth, scheduling overhead Monitoring and observability Track GPU saturation, storage I/O, job progress End-to-end pipeline visibility GPU clusters The unit of compute is the GPU, not the CPU. Modern AI training runs on clusters of H100 or A100 class GPUs connected via NVLink for intra-node communication and InfiniBand or RoCE for inter-node. Cluster size ranges from a few nodes for fine-tuning to thousands of nodes for large-scale pretraining. GPU utilization is the primary efficiency metric. Anything below 80% sustained utilization during a training run points to a bottleneck elsewhere — typically in data loading or checkpoint I/O. Infrastructure teams that optimize the compute layer without addressing the storage layer routinely see GPU utilization in the 40–60% range. High-speed networking and RDMA Remote Direct Memory Access (RDMA) is the mechanism that lets storage systems feed GPUs without CPU involvement. Traditional TCP/IP networking introduces latency and CPU overhead that becomes a hard ceiling on throughput at scale. InfiniBand and RoCEv2 fabrics running RDMA eliminate that overhead and enable GPU-direct storage access. For AI compute infrastructure, the network fabric connects three planes: GPU-to-GPU for collective operations (AllReduce during training), GPU-to-storage for data loading and checkpoint writes, and CPU-to-orchestration for job scheduling. Each plane has different latency and bandwidth requirements, and conflating them on a single fabric creates contention. Storage tiers Storage is the most commonly underspecified component in AI compute infrastructure. Teams overbuy GPU compute, then discover that training jobs are starved for data. A well-designed storage tier provides: GPU-direct tier: Ultra-low-latency flash connected via S3 over RDMA. Feeds live training runs, KV cache for inference, and real-time checkpoint writes. Latency target: under 50 microseconds. Hot tier: High-density QLC or NL-SSD for active datasets, embeddings, and recent checkpoints. Delivers multi-terabyte-per-second aggregate throughput for parallel GPU data loading. Warm tier: For datasets used less frequently — staged training data, intermediate embeddings, RAG indices. Cold tier: Long-term retention of raw training data, regulatory archives, and model lineage artifacts. The tiered storage for AI design pattern matches data temperature to storage economics while preserving the throughput the hot path needs. Orchestration Kubernetes and Slurm are both used in production AI compute infrastructure, and they serve different purposes. Slurm dominates HPC-style training clusters where jobs are large, long-running, and require tight resource reservation. Kubernetes is preferred for inference serving, data preparation pipelines, and mixed workloads where dynamic scheduling matters. The choice of orchestration layer affects how storage is provisioned. Kubernetes-native AI workloads use persistent volume claims backed by S3-compatible object storage. Slurm jobs typically mount storage directly. Infrastructure teams building certified AI infrastructure pipelines need storage that works cleanly with both paradigms. Monitoring and observability GPU saturation tells you whether the infrastructure is working; storage I/O tells you where the bottleneck is. Effective observability spans compute metrics (GPU utilization, memory bandwidth, NVLink throughput), storage metrics (read/write throughput per tier, queue depth, cache hit rates), and job-level metrics (data loading time, checkpoint write latency, epoch duration). Design principles for AI compute infrastructure Three principles separate infrastructure that scales from infrastructure that requires constant firefighting. Disaggregation Disaggregation means separating compute and storage so each scales independently. Hyperconverged infrastructure made sense for general enterprise workloads. For AI, it forces an uncomfortable choice: scale storage alongside compute even when you only need one, or cap one to match the other. Disaggregated architectures let teams add GPU nodes without buying storage, and expand storage capacity without adding GPU cycles. The storage cluster connects to the compute cluster over the high-speed fabric. This is how AI data centers at scale are designed today. Scale-out over scale-up Horizontal scaling via additional nodes beats vertical scaling via larger nodes for AI workloads. Scale-out object storage clusters provide linear throughput growth as nodes are added. A single large NAS head unit becomes a throughput bottleneck at petabyte scale; a distributed object store does not. For training workloads that read from hundreds of files simultaneously across thousands of GPUs, the aggregate I/O bandwidth of a scale-out cluster is the only architecture that avoids storage as the bottleneck. Workload-aligned storage tiers Not every AI workload has the same storage requirements. Checkpoint I/O during training requires high write throughput and low latency. Dataset ingestion requires sustained high read bandwidth. RAG retrieval requires fast random reads. Long-term model archival requires density and durability, not speed. Designing a single storage tier that tries to handle all of these is expensive and ineffective. Workload-aligned tiers match the storage profile to the workload requirements, then use lifecycle automation to move data between tiers without manual intervention. Multimodal AI data storage workloads in particular benefit from this approach — image, video, and text datasets have very different access patterns. Storage design for AI compute workloads The AI data lifecycle runs through five stages, each with distinct storage requirements. Ingest and preparation: Raw training data arrives from external sources, data lakes, or instrument feeds. Ingest storage needs high write bandwidth and enough capacity to hold large working sets. Data preparation — cleaning, tokenizing, formatting — runs CPU-intensive jobs that read and rewrite data repeatedly. Object storage with a flat namespace handles this efficiently at scale; the AI data storage without roadblocks pattern covers the ingest-to-training path in detail. Training datasets: Once data is prepared, training jobs read it repeatedly across many epochs. The storage system needs to sustain high aggregate throughput for parallel reads from many GPUs simultaneously. Caching hot datasets on flash tiers reduces re-read latency significantly. Checkpoints: Training runs write checkpoints periodically to recover from failures. Checkpoint writes are bursty, high-bandwidth, and latency-sensitive — a slow checkpoint stalls the entire training run. The GPU-direct tier is typically used for in-flight checkpoints; completed checkpoints are promoted to the hot tier. Inference serving: Inference has different storage requirements than training. Model weights need fast load times at startup. KV cache for long-context inference requires ultra-low-latency random reads. Vector database storage and retrieval-augmented generation storage for RAG pipelines sit on the hot tier with performance profiles closer to a database than a file system. Retention and archival: Trained models, evaluation results, and raw datasets have long retention requirements driven by governance, reproducibility, and regulatory compliance. Cold-tier storage with strong durability guarantees and automated lifecycle policies handles this without manual data management overhead. How Scality ADI integrates with AI compute infrastructure Scality ADI (Autonomous Data Infrastructure) is purpose-built for the storage layer in AI compute infrastructure. It addresses the specific failure modes that occur when general-purpose storage is asked to handle petabyte-scale AI workloads. Architecture. Scality ADI runs on the RING10 disaggregated architecture — an object-native distributed storage engine designed for extreme petabyte-to-exabyte environments. RING10 provides a unified namespace, strong durability, and automated lifecycle management across all storage tiers. ScalityOS is the hardened, standardized runtime that creates an appliance-like experience across the hardware stack, reducing lifecycle fragmentation and simplifying operations. The relationship between layers: Scality RING is the engine, ScalityOS is the chassis, Scality ADI is the platform. Four workload-aligned storage tiers. Scality ADI ships with four pre-configured tiers mapped to the AI data lifecycle stages described above: GPU-Direct tier — TLC flash with S3 over RDMA. Sub-50-microsecond latency for live training runs, checkpoint I/O, and KV cache. Connects directly to the GPU fabric without CPU involvement. Hot tier — QLC and NL-SSD delivering multi-terabyte-per-second aggregate throughput. For active training datasets, embeddings, and recent checkpoints. Warm tier — For staged datasets, RAG indices, and intermediate embeddings. Cold tier — High-density capacity for raw data archives, regulatory retention, and model lineage. Lifecycle automation moves data between tiers without creating new storage silos. The high-density power consumption comparison between HDD and QLC flash covers the economics of warm-to-cold transitions in detail. GPU-direct via S3 over RDMA. Scality ADI supports GPU-direct storage access via S3 over RDMA, which means GPU nodes read training data directly from the storage fabric without CPU involvement. This eliminates the CPU bottleneck that limits throughput in TCP-based storage architectures and is the mechanism that enables sustained GPU utilization above 90% during data-intensive training runs. Workload-aligned software-hardware profiles. Rather than requiring infrastructure teams to tune storage configurations for each workload type, Scality ADI ships with pre-validated profiles that match hardware configuration to workload requirements. A team deploying a new fine-tuning cluster selects the appropriate profile; the platform configures the right tier, network parameters, and durability settings automatically. Infrastructure teams building agentic AI storage infrastructure on Scality ADI get a platform that handles the full AI data flow — from ingest through preparation, RAG and VSS workloads, checkpoints, embeddings, logs, and long-term retention — without the operational complexity of managing separate storage systems for each stage. See how Scality ADI integrates with AI compute infrastructure → Frequently asked questions What is AI compute infrastructure? AI compute infrastructure is the integrated stack of GPU clusters, high-speed networking, storage tiers, and orchestration software that supports machine learning training, inference, and data preparation at scale. It differs from general-purpose data center infrastructure in that workloads are GPU-centric, data volumes grow rapidly, and I/O patterns are bursty and parallel throughout the AI data lifecycle. What are the key components of AI compute infrastructure? The five primary components are GPU clusters (parallel compute), high-speed networking with RDMA support (low-latency data movement), workload-aligned storage tiers (data feeding and retention), orchestration software such as Kubernetes or Slurm (job scheduling and resource management), and monitoring and observability tooling (GPU utilization and I/O performance tracking). Storage is frequently the most underspecified component and the most common source of GPU underutilization. How is AI compute infrastructure different from traditional data center infrastructure? Traditional data center infrastructure is designed for CPU-centric, latency-tolerant workloads with moderate and predictable I/O. AI compute infrastructure must sustain extremely high aggregate storage throughput to keep GPU clusters fed, handle bursty checkpoint writes without stalling training jobs, and scale storage capacity independently of compute. The networking fabric also differs: AI infrastructure requires RDMA-capable fabrics to eliminate CPU overhead on the data path, whereas traditional data centers run TCP/IP networking throughout. What storage architecture works best for AI compute workloads? Disaggregated, scale-out object storage with workload-aligned tiers performs best for AI compute workloads. The storage cluster connects to the compute cluster over a high-speed RDMA fabric rather than being co-located with GPU nodes. Multiple tiers — GPU-direct flash, hot QLC/NL-SSD, warm, and cold — serve different stages of the AI data lifecycle. Lifecycle automation moves data between tiers without manual intervention. This architecture avoids the throughput ceilings that emerge from hyperconverged designs and scales horizontally as GPU cluster size grows. See storage capacity planning for guidance on sizing each tier. Further reading Tiered storage for AI: scalable performance and cost control Agentic AI storage infrastructure AI data center AI data storage without roadblocks Certified AI infrastructure pipeline Retrieval augmented generation and storage for AI Object storage vs block storage
