{"id":75667,"date":"2026-11-10T17:04:00","date_gmt":"2026-11-10T11:34:00","guid":{"rendered":"https:\/\/cyfuture.cloud\/blog\/?p=75667"},"modified":"2026-11-10T17:05:25","modified_gmt":"2026-11-10T11:35:25","slug":"cloud-storage-in-ai-factories-why-your-gpus-are-only-as-fast-as-your-data","status":"publish","type":"post","link":"https:\/\/cyfuture.cloud\/blog\/cloud-storage-in-ai-factories-why-your-gpus-are-only-as-fast-as-your-data\/","title":{"rendered":"<strong>Cloud Storage in AI Factories: Why Your GPUs Are Only as Fast as Your Data<\/strong>"},"content":{"rendered":"<div id=\"toc_container\" class=\"no_bullets\"><p class=\"toc_title\">Table of Contents<\/p><ul class=\"toc_list\"><li><a href=\"#The_GPU_cluster_that_was_always_waiting\">The GPU cluster that was always waiting<\/a><\/li><li><a href=\"#What_is_an_AI_factory_really\">What is an AI factory, really?<\/a><\/li><li><a href=\"#High-density_GPU_infrastructure_and_the_data_hunger_problem\">High-density GPU infrastructure and the \u201cdata hunger\u201d problem<\/a><\/li><li><a href=\"#Storage_performance_in_plain_language\">Storage performance in plain language<\/a><\/li><li><a href=\"#Which_storage_type_does_what\">Which storage type does what?<\/a><\/li><li><a href=\"#Why_object_storage_anchors_the_data_lake\">Why object storage anchors the data lake<\/a><\/li><li><a href=\"#When_many_GPUs_read_at_once\">When many GPUs read at once<\/a><\/li><li><a href=\"#The_formula_behind_idle_GPUs\">The formula behind idle GPUs<\/a><\/li><li><a href=\"#Checkpointing_insurance_with_a_price_tag\">Checkpointing: insurance with a price tag<\/a><\/li><li><a href=\"#Inference_has_different_needs\">Inference has different needs<\/a><\/li><li><a href=\"#Storage_does_not_work_alone\">Storage does not work alone<\/a><\/li><li><a href=\"#A_tiered_design_that_makes_sense\">A tiered design that makes sense<\/a><\/li><li><a href=\"#Security_and_governance_without_slowing_things_down\">Security and governance without slowing things down<\/a><\/li><li><a href=\"#The_real_cost_of_storage\">The real cost of storage<\/a><\/li><li><a href=\"#Build_AI-ready_storage_with_Cyfuture_Cloud\">Build AI-ready storage with Cyfuture Cloud<\/a><\/li><li><a href=\"#AI_storage_evaluation_framework\">AI storage evaluation framework<\/a><ul><li><ul><li><a href=\"#Deployment_checklist\">Deployment checklist<\/a><\/li><\/ul><\/li><li><a href=\"#Conclusion\">Conclusion<\/a><\/li><\/ul><\/li><\/ul><\/div>\n\n<p><b><i>Cloud storage in AI factories is the layer that feeds data to GPUs, saves training checkpoints, serves models for inference and keeps everything governed. If storage cannot deliver data fast enough, expensive GPUs sit idle. In an AI factory, storage is part of the compute pipeline, not a passive archive.<\/i><\/b><\/p>\n<h2><span id=\"The_GPU_cluster_that_was_always_waiting\"><b>The GPU cluster that was always waiting<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Picture a rack of GPUs, powered up, cooled and ready. The dashboard shows jobs running, yet utilisation keeps dipping. Nothing is broken. The GPUs are simply waiting for data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This happens more often than teams admit. Cloud storage in AI factories decides whether that expensive compute works or waits. So let\u2019s look at how.<\/span><\/p>\n<h2><span id=\"What_is_an_AI_factory_really\"><b>What is an AI factory, really?<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">An AI factory is a purpose-built environment that turns data into AI services at scale. It combines data pipelines, compute, networking, software, governance, power, cooling and operations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Think of a physical factory. Raw material comes in, machines process it, and finished goods go out. Here, data is the raw material, GPUs are the machines, and models and predictions are the output.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Storage sits at every stage: ingestion, preparation, training, fine-tuning, inference, monitoring and archival. That is why it cannot be an afterthought.<\/span><\/p>\n<h2><span id=\"High-density_GPU_infrastructure_and_the_data_hunger_problem\"><b>High-density GPU infrastructure and the \u201cdata hunger\u201d problem<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">High-density <\/span><a href=\"https:\/\/cyfuture.cloud\/gpu-cloud-infrastructure\"><span style=\"font-weight: 400;\">GPU infrastructure<\/span><\/a><span style=\"font-weight: 400;\"> packs many GPUs and servers into a compact rack or cluster. You get more compute per unit of space, power and cooling. It also brings heavier thermal loads, which is why liquid or hybrid cooling often enters the conversation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here\u2019s the catch. The faster the compute, the hungrier it gets for data. Dense clusters amplify every weakness in the storage and network path.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The cause-and-effect chain is simple:<\/span><\/p>\n<p><b><i>Data availability \u2192 GPU feeding efficiency \u2192 compute utilisation \u2192 AI workload performance<\/i><\/b><\/p>\n<p><span style=\"font-weight: 400;\">Break any link and the rest suffers.<\/span><\/p>\n<h2><span id=\"Storage_performance_in_plain_language\"><b>Storage performance in plain language<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Before comparing storage types, get the vocabulary straight.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Throughput:<\/b><span style=\"font-weight: 400;\"> how much data moves per second<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>IOPS:<\/b><span style=\"font-weight: 400;\"> input\/output operations per second, which matters for many small reads and writes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Latency:<\/b><span style=\"font-weight: 400;\"> the delay before a request completes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Concurrency:<\/b><span style=\"font-weight: 400;\"> how many requests run at once<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Metadata performance:<\/b><span style=\"font-weight: 400;\"> how quickly the system handles file names, permissions and directories<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Think of a highway. Throughput is its width, latency is the time taken to join it, and concurrency is how many vehicles travel together.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Different jobs stress different measures. Large sequential reads need throughput. Millions of small files need IOPS and metadata speed. Real-time inference needs low latency.<\/span><\/p>\n<h2><span id=\"Which_storage_type_does_what\"><b>Which storage type does what?<\/b><\/span><\/h2>\n<p>\u00a0<\/p>\n<table border=\"2\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\">\n<p><b>Storage type<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>Best suited for<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>AI use cases<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Object storage<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Massive unstructured data, durability<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Raw datasets, checkpoints, model artefacts, backups<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">File storage<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Shared access, collaborative workflows<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Data preparation, shared project data<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Block storage<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Low-latency, persistent volumes<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Databases, VM-based workloads<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Parallel file system<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">High throughput with many concurrent clients<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Distributed training, HPC<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Cache and tiered storage<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Hot, frequently used data<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">GPU-adjacent datasets<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Archive storage<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Long-term, low-cost retention<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Compliance records, historical data<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">No single type does it all. Most AI factories use several together.<\/span><\/p>\n<h2><span id=\"Why_object_storage_anchors_the_data_lake\"><b>Why object storage anchors the data lake<\/b><\/span><\/h2>\n<p><a href=\"https:\/\/cyfuture.cloud\/object-storage-cloud\"><span style=\"font-weight: 400;\">Object storage<\/span><\/a><span style=\"font-weight: 400;\"> scales to huge volumes of images, video, audio, documents, logs and sensor data. It stores each item with metadata, which helps with discovery, classification, lineage and governance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That makes it the natural home for training datasets, checkpoints, embeddings and archives. It is rarely the fastest tier, though. Which brings us to the next piece.<\/span><\/p>\n<h2><span id=\"When_many_GPUs_read_at_once\"><b>When many GPUs read at once<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Large training jobs have hundreds of workers pulling from the same dataset. A single storage node becomes a bottleneck very quickly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A parallel file system spreads data across multiple storage nodes through striping, so many clients read in parallel. It also has to manage metadata servers, locking, data locality and cache coherence. Done well, it removes the queue. Done poorly, it creates a new one.<\/span><\/p>\n<h2><span id=\"The_formula_behind_idle_GPUs\"><b>The formula behind idle GPUs<\/b><\/span><\/h2>\n<p><b><i>GPU utilisation = time spent on useful computation \u00f7 total time (computation + data access + waiting)<\/i><\/b><\/p>\n<p><span style=\"font-weight: 400;\">This shows the problem clearly. Every second a GPU spends waiting for data drags the ratio down, and you still pay for that second.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Common culprits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Slow dataset loading<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Not enough storage bandwidth<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">High metadata latency<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Millions of tiny files<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Network congestion<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Checkpoint contention<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Some fixes are simple. Use efficient formats such as Parquet or WebDataset. Shard data so workers read in parallel. Prefetch the next batch while the current one computes. Cache hot data close to the GPUs.<\/span><\/p>\n<h2><span id=\"Checkpointing_insurance_with_a_price_tag\"><b>Checkpointing: insurance with a price tag<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Long training runs fail. Hardware faults, network drops and software errors all happen. A checkpoint saves the model weights, optimiser state, learning-rate state, step count, random seeds and dataset position, so training resumes instead of restarting.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But checkpoints are heavy writes, and many GPUs writing together can stall a run.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The trade-off is straightforward. Frequent checkpoints shrink the work lost on failure but load the storage. Rare checkpoints ease storage but risk expensive retraining. Asynchronous and incremental checkpointing, plus writing first to a fast tier, help balance the two.<\/span><\/p>\n<h2><span id=\"Inference_has_different_needs\"><b>Inference has different needs<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Training wants throughput. Inference wants speed and consistency.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Serving platforms need quick access to model files, versions, embeddings, vector databases and context for retrieval-augmented generation (RAG), where a model pulls in relevant documents before answering. Logs and audit trails matter too.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Not all of it needs the top tier. Keep hot model files and embeddings on fast storage, and let older logs and versions move down.<\/span><\/p>\n<h2><span id=\"Storage_does_not_work_alone\"><b>Storage does not work alone<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Even the fastest tier disappoints if the network path is weak. Bandwidth, latency, jitter, packet loss, congestion and protocol overhead all count. Cross-zone or cross-region movement adds delay and cost.<\/span><\/p>\n<p><b><i>Effective transfer time \u2248 dataset size \u00f7 available bandwidth + protocol and network overhead<\/i><\/b><\/p>\n<p><span style=\"font-weight: 400;\">In short, a big dataset on a thin pipe takes a long time, whatever sits at the other end. Always evaluate storage together with the network.<\/span><\/p>\n<h2><span id=\"A_tiered_design_that_makes_sense\"><b>A tiered design that makes sense<\/b><\/span><\/h2>\n<p>\u00a0<\/p>\n<table border=\"2\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\">\n<p><b>Tier<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>Purpose<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>Typical data<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>Performance need<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Hot<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Active training and inference<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Current datasets, checkpoints, embeddings<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">High throughput, low latency<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Warm<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Recent, frequently used data<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Intermediate results, shared assets<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Balanced<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Cold<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Retention and compliance<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Historical datasets, audit records<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Cost-efficient durability<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Archive<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Long-term preservation<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Regulatory records, backups<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Low cost, durable<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">Lifecycle policies should move data between tiers based on access frequency, compliance needs and business value. Set the rules once and let automation do the work.<\/span><\/p>\n<p><a href=\"https:\/\/cyfuture.cloud\/gpus\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-75668 size-full\" src=\"https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Is-your-storage-keeping-up-with-your-GPUs-cta-final.jpg\" alt=\"GPUs Server\" width=\"970\" height=\"270\" srcset=\"https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Is-your-storage-keeping-up-with-your-GPUs-cta-final.jpg 970w, https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Is-your-storage-keeping-up-with-your-GPUs-cta-final-300x84.jpg 300w, https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Is-your-storage-keeping-up-with-your-GPUs-cta-final-768x214.jpg 768w\" sizes=\"(max-width: 970px) 100vw, 970px\" \/><\/a><\/p>\n\n\n\n<h2><span id=\"Security_and_governance_without_slowing_things_down\"><b>Security and governance without slowing things down<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AI storage often holds sensitive data, so control matters as much as speed. Plan for:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Encryption at rest and in transit<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identity and access management with least-privilege access<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data classification, lineage and versioning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Immutable storage and audit trails<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Backup, disaster recovery and data residency<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Retention policies and secure deletion<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tenant isolation in shared environments<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Neither speed nor control should be traded away for the other. Design both from day one.<\/span><\/p>\n<h2><span id=\"The_real_cost_of_storage\"><b>The real cost of storage<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Price per gigabyte is the smallest part of the story. Performance tier, API requests, egress, redundancy, snapshots, checkpoint frequency, duplicate data and operational effort all add up.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here\u2019s the practical rule. A cheaper tier that leaves GPUs idle can cost more overall than a faster tier that keeps them busy. Judge total cost of ownership, not the storage line item.<\/span><\/p>\n<h2><span id=\"Build_AI-ready_storage_with_Cyfuture_Cloud\"><b>Build AI-ready storage with Cyfuture Cloud<\/b><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Cyfuture Cloud helps organisations align storage, compute, networking, security and operations for AI workloads. Support spans scalable <\/span><a href=\"https:\/\/cyfuture.cloud\/storage\"><span style=\"font-weight: 400;\">cloud storage<\/span><\/a><span style=\"font-weight: 400;\">, GPU infrastructure, high-performance compute, secure data management, storage and compute integration, governance, backup and resilience, managed infrastructure support and technical consultation.<\/span><\/p>\n<h2><span id=\"AI_storage_evaluation_framework\"><b>AI storage evaluation framework<\/b><\/span><\/h2>\n<p>\u00a0<\/p>\n<table border=\"2\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\">\n<p><b>Area<\/b><\/p>\n<\/td>\n<td style=\"text-align: center;\">\n<p><b>Questions to ask<\/b><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Workload<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Training, inference, fine-tuning or HPC?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Data profile<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">What are the dataset size, file count and file-size distribution?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Performance<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">What throughput, IOPS, latency and concurrency are needed?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Access pattern<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Sequential, random, concurrent or metadata-heavy?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Checkpointing<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">How often are checkpoints written and restored?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Networking<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Can the storage network handle peak GPU demand?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Governance<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">Are security, retention, lineage and compliance defined?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Cost<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">What is the total cost, including performance and egress?<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span style=\"font-weight: 400;\">Resilience<\/span><\/p>\n<\/td>\n<td>\n<p><span style=\"font-weight: 400;\">How fast can the system recover from failure?<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4><span id=\"Deployment_checklist\"><b>Deployment checklist<\/b><\/span><\/h4>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Define the workload type.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Estimate dataset size and growth.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Map the file-size distribution.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Measure required throughput, IOPS and latency.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Size checkpoints and set their frequency.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Plan ingestion and preprocessing.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Select storage tiers and lifecycle policies.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Check network bandwidth and latency.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Set security, access and backup requirements.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Estimate total cost of ownership.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Benchmark with representative workloads.<\/span><\/li>\n<\/ol>\n<h3><span id=\"Conclusion\"><b>Conclusion<\/b><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Cloud storage in AI factories is not a back-room utility. It decides how busy your GPUs stay, how safely long jobs run, how quickly models respond and how well data is governed.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So audit the data path before you add more GPUs. Measure, benchmark and tier with intent. The fastest cluster in the world is only as productive as the data it receives.<\/span><\/p>\n<p><a href=\"https:\/\/cyfuture.cloud\/b300-gpu-server\"><img decoding=\"async\" loading=\"lazy\" class=\"alignnone wp-image-75669 size-full\" src=\"https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Cyfuture-Cloud-CTA-2-2.jpg\" alt=\"B300 GPU Server  \" width=\"970\" height=\"270\" srcset=\"https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Cyfuture-Cloud-CTA-2-2.jpg 970w, https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Cyfuture-Cloud-CTA-2-2-300x84.jpg 300w, https:\/\/cyfuture.cloud\/blog\/cyft-uploads\/2026\/11\/Cyfuture-Cloud-CTA-2-2-768x214.jpg 768w\" sizes=\"(max-width: 970px) 100vw, 970px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Table of ContentsThe GPU cluster that was always waitingWhat is an AI factory, really?High-density GPU infrastructure and the \u201cdata hunger\u201d problemStorage performance in plain languageWhich storage type does what?Why object storage anchors the data lakeWhen many GPUs read at onceThe formula behind idle GPUsCheckpointing: insurance with a price tagInference has different needsStorage does not work [&hellip;]<\/p>\n","protected":false},"author":29,"featured_media":75671,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[501],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/posts\/75667"}],"collection":[{"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/users\/29"}],"replies":[{"embeddable":true,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/comments?post=75667"}],"version-history":[{"count":4,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/posts\/75667\/revisions"}],"predecessor-version":[{"id":75675,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/posts\/75667\/revisions\/75675"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/media\/75671"}],"wp:attachment":[{"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/media?parent=75667"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/categories?post=75667"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cyfuture.cloud\/blog\/wp-json\/wp\/v2\/tags?post=75667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}