{"id":9811,"date":"2026-09-20T19:46:42","date_gmt":"2026-09-21T00:46:42","guid":{"rendered":"https:\/\/www.serverpronto.com\/spu\/2026\/09\/how-to-optimize-gpu-for-ai-training\/"},"modified":"2026-09-20T19:46:42","modified_gmt":"2026-09-21T00:46:42","slug":"how-to-optimize-gpu-for-ai-training","status":"publish","type":"post","link":"https:\/\/www.serverpronto.com\/spu\/2026\/09\/how-to-optimize-gpu-for-ai-training\/","title":{"rendered":"How to Optimize GPU for AI Training: 2026 Guide"},"content":{"rendered":"<h2 id=\"table-of-contents\">Table of Contents<\/h2>\n<ul>\n<li><a href=\"#why-most-ai-training-gpus-run-at-5-use\">Why Most AI Training GPUs Run at 5% Use<\/a><\/li>\n<li><a href=\"#what-youll-need-before-you-optimize-gpu-performance\">What You&#8217;ll Need Before You Optimize GPU Performance<\/a><\/li>\n<li><a href=\"#step-1-profile-your-pipeline-to-find-the-real-bottleneck\">Step 1: Profile Your Pipeline to Find the Real Bottleneck<\/a>\n<ul>\n<li><a href=\"#reading-use-memory-and-throughput-metrics\">Reading Use, Memory, and Throughput Metrics<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#step-2-fix-data-pipeline-bottlenecks-before-touching-the-model\">Step 2: Fix Data Pipeline Bottlenecks Before Touching the Model<\/a><\/li>\n<li><a href=\"#step-3-tune-batch-size-optimization-and-gradient-accumulation\">Step 3: Tune Batch Size Optimization and Gradient Accumulation<\/a><\/li>\n<li><a href=\"#step-4-apply-mixed-precision-training-to-cut-memory-and-time\">Step 4: Apply Mixed Precision Training to Cut Memory and Time<\/a>\n<ul>\n<li><a href=\"#fp16-bf16-and-int8-which-precision-to-pick\">FP16, BF16, and INT8: Which Precision to Pick<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#step-5-memory-parallelism-and-cluster-orchestration-at-scale\">Step 5: Memory, Parallelism, and Cluster Orchestration at Scale<\/a><\/li>\n<li><a href=\"#common-mistakes-that-waste-gpu-hours\">Common Mistakes That Waste GPU Hours<\/a><\/li>\n<li><a href=\"#frequently-asked-questions\">Frequently Asked Questions<\/a><\/li>\n<\/ul>\n<p><em>Last Updated: September 20, 2026<\/em><\/p>\n<h2 id=\"why-most-ai-training-gpus-run-at-5-use\">Why Most AI Training GPUs Run at 5% Use<\/h2>\n<p>Here is the number that should bother every ML team: industry-wide GPU use for AI workloads sits at just 5%, according to <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/cast.ai\/reports\/state-of-kubernetes-optimization\/\">Cast AI&#8217;s 2026 State of Kubernetes Optimization Report<\/a>. That means the hardware you are paying for sits idle 95% of the time. Learning how to optimize gpu for <a href=\"\/spu\/2026\/09\/dedicated-gpu-servers-for-ai-training\/\">ai training<\/a> is not a tuning exercise, it is the difference between a viable model budget and a runaway one.<\/p>\n<p>At ServerPronto, we have provisioned GPU and bare metal environments for teams running exactly these workloads, and the pattern is consistent. The bottleneck is almost never the chip. It is the pipeline feeding it.<\/p>\n<p>Below, we break down a step-by-step process to find and fix the real constraint. The payoff is real: state-of-the-art GPUs can cut training times by up to 60% versus previous generations, per <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/mammothclub.com\/blog\/artificial-intelligence-class\">Mammoth Club&#8217;s 2026 analysis of AI hardware demands<\/a>, but only if data arrives fast enough to keep them fed.<\/p>\n<h2 id=\"what-youll-need-before-you-optimize-gpu-performance\">What You&#8217;ll Need Before You Optimize GPU Performance<\/h2>\n<p><strong>Total Time:<\/strong> 2-4 hours for initial profiling and first fixes.<br \/>\n<strong>Difficulty:<\/strong> Intermediate.<\/p>\n<p>Before you change a single flag, confirm you have these in place:<\/p>\n<ul>\n<li>A profiling tool (Nsight Systems, PyTorch Profiler, or nvidia-smi for quick checks)<\/li>\n<li>Access to your full data pipeline, from storage to the data loader<\/li>\n<li>A baseline run with logged throughput, latency, and memory footprint<\/li>\n<li><a href=\"\/spu\/2026\/09\/buy-dedicated-server-with-root-access\/\">Root access<\/a> to the training environment so you can tune drivers and CUDA versions<\/li>\n<li>A rollback plan, because some changes help one model and hurt another<\/li>\n<\/ul>\n<p>That last point matters more than most guides admit.<\/p>\n<div style=\"margin:1.5rem 0;padding:16px 20px;background-color:#fffbeb;border-left:4px solid #fde68a;border-radius:0 8px 8px 0\">\n<strong style=\"display:block;margin-bottom:4px;color:#111827;font-size:14px\"> Watch Out<\/strong><br \/>\n<span style=\"color:#374151;font-size:15px;line-height:1.6\">Changing precision or batch size without a saved baseline means you cannot prove a fix worked. Teams routinely &#8220;optimize&#8221; for a week and end up slower than where they started.<\/span>\n<\/div>\n<h2 id=\"step-1-profile-your-pipeline-to-find-the-real-bottleneck\">Step 1: Profile Your Pipeline to Find the Real Bottleneck<\/h2>\n<p>Never optimize blind. Run a short profiling pass and classify your workload as compute-bound, memory-bound, or IO-bound. That single label decides everything that follows.<\/p>\n<figure class=\"article-content-image my-8\" style=\"margin:2em 0;padding:0;background:transparent;border:0\"><img decoding=\"async\" src=\"https:\/\/cdn.grandranker.com\/articles\/how-to-optimize-gpu-for-ai-training-2026-guide-content-1-1789942304.jpg\" alt=\"An engineer at a workstation with multiple monitors showing GPU use graphs and terminal output, server racks visible in the background of a data center\" class=\"w-full rounded-lg shadow-lg\" loading=\"lazy\" style=\"display:block;width:100%;max-width:100%;height:auto;border-radius:8px;margin:0 auto\"><figcaption class=\"text-sm text-gray-600 mt-2 text-center\" style=\"font-size:0.875em;color:inherit;opacity:0.75;text-align:center;margin-top:0.6em\">An engineer at a workstation with multiple monitors showing GPU use graphs and terminal output, server racks visible in the background of a data center<\/figcaption><\/figure>\n<h3 id=\"reading-use-memory-and-throughput-metrics\">Reading Use, Memory, and Throughput Metrics<\/h3>\n<p>Start with three numbers:<\/p>\n<ul>\n<li><strong>GPU use:<\/strong> if it sits below 80%, your GPU is waiting on something else<\/li>\n<li><strong>Memory footprint:<\/strong> if you are near the HBM ceiling, you are memory-bound<\/li>\n<li><strong>Throughput:<\/strong> samples per second tells you whether fixes actually help<\/li>\n<\/ul>\n<p>If use is low but memory is fine, the problem is upstream. If memory is maxed, the problem is the model or batch size. That distinction saves hours.<\/p>\n<div style=\"margin:1.5rem 0;padding:16px 20px;background-color:#f0f9ff;border-left:4px solid #bae6fd;border-radius:0 8px 8px 0\">\n<strong style=\"display:block;margin-bottom:4px;color:#111827;font-size:14px\"> Pro Tip<\/strong><br \/>\n<span style=\"color:#374151;font-size:15px;line-height:1.6\">Profile for 100-200 steps, not 10. Early training steps have warm-up overhead that makes short profiles lie about steady-state throughput.<\/span>\n<\/div>\n<h2 id=\"step-2-fix-data-pipeline-bottlenecks-before-touching-the-model\">Step 2: Fix Data Pipeline Bottlenecks Before Touching the Model<\/h2>\n<p>Data pipeline bottlenecks are the most common cause of idle GPUs. The fix is usually cheaper and faster than any model change.<\/p>\n<div class=\"video-embed-container my-8 bg-gray-50 rounded-lg p-4\">\n<p><iframe loading=\"lazy\" title=\"Making GPUs Actually Fast: A Deep Dive into Training Performance\" width=\"770\" height=\"433\" src=\"https:\/\/www.youtube.com\/embed\/pHqcHzxx6I8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<div class=\"video-info mt-3\">\n<h4 class=\"text-lg font-semibold text-gray-800\"><a href=\"https:\/\/www.youtube.com\/watch?v=pHqcHzxx6I8\" target=\"_blank\" rel=\"noopener\">Making GPUs Actually Fast: A Deep Dive into Training Performance<\/a><\/h4>\n<p class=\"text-sm text-gray-600 mt-1\">Jane Street<\/p>\n<\/p><\/div>\n<\/div>\n<p>Work through these in order:<\/p>\n<ol>\n<li>Increase your data loader worker count until CPU usage plateaus<\/li>\n<li>Enable pre-fetching so the next batch loads while the current one trains<\/li>\n<li>Move data to local NVMe or a fast parallel file system instead of network storage<\/li>\n<li>Pre-process and cache static transforms once, not every epoch<\/li>\n<\/ol>\n<p>A slow storage layer is the silent killer here. If your data loader waits on disk, no amount of GPU tuning helps.<\/p>\n<h2 id=\"step-3-tune-batch-size-optimization-and-gradient-accumulation\">Step 3: Tune Batch Size Optimization and Gradient Accumulation<\/h2>\n<p>Batch size optimization is the highest-use knob most teams leave untouched. Bigger batches raise throughput, but only until memory runs out.<\/p>\n<p>The practical approach:<\/p>\n<p class=\"cta-inline\" style=\"background-color: #2563eb08;border-left: 4px solid #2563eb;padding: 16px 20px;margin: 24px 0;border-radius: 0 8px 8px 0\">\n     <a href=\"https:\/\/www.serverpronto.com\/\" style=\"color: #2563eb;font-weight: 600;text-decoration: underline\">Build &amp; Price ?<\/a>\n<\/p>\n<ul>\n<li>Increase batch size until you hit the HBM limit<\/li>\n<li>If you need a larger effective batch, use gradient accumulation instead<\/li>\n<li>Re-tune your learning rate whenever you change effective batch size<\/li>\n<\/ul>\n<p>Strategic optimization along these lines can raise GPU memory use by 2-3x, according to <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.mirantis.com\/blog\/inference-costs\/\">Mirantis research on data loading and batch sizing<\/a>. That is not a rounding error. It is the difference between one node and three.<\/p>\n<h2 id=\"step-4-apply-mixed-precision-training-to-cut-memory-and-time\">Step 4: Apply Mixed Precision Training to Cut Memory and Time<\/h2>\n<p>Mixed precision training is the practice of running most operations in FP16 or BF16 while keeping a master copy of weights in FP32. It cuts memory footprint and speeds up math on tensor cores, usually with no accuracy loss.<\/p>\n<p>Modern training and inference standards now prioritize FP16 or FP8 performance, per <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.sabrepc.com\/blog\/Deep-Learning-and-AI\/machine-learning-memory-requirements?srsltid=AU7gw4Vbr4jAiaJxJ5OZgCk-TkF8-jnFNYRgW1GGZyqAwSZ20SPJ8JHp\">SabrePC&#8217;s 2026 deep learning hardware guide<\/a>, which tells you where the hardware is designed to run fastest.<\/p>\n<h3 id=\"fp16-bf16-and-int8-which-precision-to-pick\">FP16, BF16, and INT8: Which Precision to Pick<\/h3>\n<table style=\"width:100%;border-collapse:collapse;margin:2rem 0;font-size:14px;line-height:1.6\">\n<thead style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">\n<tr>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Precision<\/th>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Best For<\/th>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Trade-off<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">FP16<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Most training runs<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Needs loss scaling<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">BF16<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Large models, stable training<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Slightly less precise<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">INT8<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Inference optimization<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Training usually needs FP16\/BF16<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Start with BF16 if your hardware supports it. It is more forgiving than FP16 and rarely needs loss scaling tweaks.<\/p>\n<h2 id=\"step-5-memory-parallelism-and-cluster-orchestration-at-scale\">Step 5: Memory, Parallelism, and Cluster Orchestration at Scale<\/h2>\n<p>Single-GPU tuning has a ceiling. Past that point, you scale out with data parallelism, model parallelism, or a mix of both, and you manage it with cluster orchestration.<\/p>\n<p>Recent work on <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/arxiv.org\/abs\/2603.11101\">thousand-GPU large-scale training recipes<\/a> shows that careful orchestration, not raw hardware, is what separates efficient clusters from expensive ones. Model compression techniques like LoRA and quantization are also lowering HBM needs on single GPUs, according to <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/docs.cloud.google.com\/docs\">Google Cloud guidance on model compression<\/a>.<\/p>\n<p>For teams running these workloads, the environment matters as much as the code. ServerPronto&#8217;s GPU and bare metal servers give you full root access, no shared resources, and 2-hour provisioning, so you can tune CUDA versions and drivers without waiting on a support ticket.<\/p>\n<h2 id=\"common-mistakes-that-waste-gpu-hours\">Common Mistakes That Waste GPU Hours<\/h2>\n<p>The same errors show up again and again. Here is the short list, with the fix for each.<\/p>\n<table style=\"width:100%;border-collapse:collapse;margin:2rem 0;font-size:14px;line-height:1.6\">\n<thead style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">\n<tr>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Mistake<\/th>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Fix<\/th>\n<th style=\"background-color:#f8f9fa;color:#111827;padding:12px 16px;text-align:left;font-weight:600;border-bottom:2px solid #e5e7eb\">Impact<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Optimizing before profiling<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Profile first, then fix<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Avoids wasted tuning<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Ignoring the data loader<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Add workers and pre-fetching<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Cuts idle GPU time<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Chasing batch size only<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Pair with gradient accumulation<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Raises effective batch<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Skipping mixed precision<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Enable BF16 or FP16<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Less memory, faster math<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Treating all workloads the same<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Match fix to bottleneck type<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e5e7eb\">Avoids regressions<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>One more: teams often assume high-end GPUs are the only path. Researchers are actively exploring lower-cost alternatives, since top hardware is both expensive and power-hungry, per <a rel=\"noopener noreferrer\" target=\"_blank\" href=\"https:\/\/www.researchgate.net\/publication\/399188705_Energy_Efficiency_Analysis_in_Modern_Manufacturing_Processes\">ResearchGate analysis of alternative efficiency methods<\/a>. Sometimes the smarter move is a better-tuned mid-tier setup, not a bigger chip.<\/p>\n<hr>\n<p>The real challenge is not knowing which knobs exist. It is having an environment where you can actually turn them without fighting shared resources, hidden overages, or slow support. ServerPronto gives you <a href=\"\/spu\/2026\/09\/dedicated-gpu-cost-machine-learning-2026\/\">dedicated GPU<\/a> and bare metal servers with full root access, transparent month-to-month pricing, and 24\/7 on-site technicians, backed by over 20 years in hosting and 42,712 provisioned servers. Get started with ServerPronto and build a training environment that keeps your GPUs busy instead of idle.<\/p>\n<section style=\"margin:3rem 0 2rem 0\">\n<h2 style=\"font-size:1.5rem;font-weight:700;margin:0 0 4px 0\" id=\"frequently-asked-questions\">Frequently Asked Questions<\/h2>\n<div style=\"padding:20px 0;border-bottom:1px solid #e5e7eb\">\n<h3 style=\"font-size:1.1rem;font-weight:600;margin:0 0 8px 0\">How do I increase GPU utilization during AI training?<\/h3>\n<div style=\"line-height:1.7;font-size:0.95rem\">\n<p style=\"margin:0\">Start by profiling with tools like Nsight Systems or PyTorch Profiler to find whether you&#8217;re compute-bound, memory-bound, or IO-bound. Industry data from Cast AI&#8217;s 2026 report shows average GPU utilization sits at just 5%, so most teams have room to gain. The biggest wins usually come from faster data loading with pre-fetching, larger batch sizes, and mixed precision training. Fix the data pipeline before buying new hardware.<\/p>\n<\/div>\n<\/div>\n<div style=\"padding:20px 0;border-bottom:1px solid #e5e7eb\">\n<h3 style=\"font-size:1.1rem;font-weight:600;margin:0 0 8px 0\">Does mixed precision training improve GPU efficiency?<\/h3>\n<div style=\"line-height:1.7;font-size:0.95rem\">\n<p style=\"margin:0\">Yes. Mixed precision training runs most operations in FP16 or BF16 while keeping critical calculations in FP32. This cuts memory footprint roughly in half and speeds up math on Tensor Cores. Modern deep learning standards now prioritize FP16 or FP8 performance, per SabrePC&#8217;s 2026 hardware guide. The tradeoff is occasional numerical instability, which loss scaling usually corrects. Test accuracy against your FP32 baseline before committing.<\/p>\n<\/div>\n<\/div>\n<div style=\"padding:20px 0;border-bottom:1px solid #e5e7eb\">\n<h3 style=\"font-size:1.1rem;font-weight:600;margin:0 0 8px 0\">What are the most common data pipeline bottlenecks in GPU training?<\/h3>\n<div style=\"line-height:1.7;font-size:0.95rem\">\n<p style=\"margin:0\">The usual culprits are slow disk reads, single-threaded data loaders, and CPU-bound augmentation. When the GPU waits on data, utilization drops and training stalls. Fixes include pre-fetching batches, using multiple worker processes, caching datasets in memory or on NVMe, and moving preprocessing to the GPU where possible. Profiling will show whether your data loader or your storage layer is the actual limit.<\/p>\n<\/div>\n<\/div>\n<div style=\"padding:20px 0;border-bottom:1px solid #e5e7eb\">\n<h3 style=\"font-size:1.1rem;font-weight:600;margin:0 0 8px 0\">How does batch size optimization affect GPU performance?<\/h3>\n<div style=\"line-height:1.7;font-size:0.95rem\">\n<p style=\"margin:0\">Larger batches improve throughput by keeping Tensor Cores busy and reducing per-step overhead, but they can hurt convergence if learning rates aren&#8217;t scaled. When memory runs out, gradient accumulation lets you simulate a larger batch across several smaller steps. Strategic optimization can raise GPU memory utilization by 2-3x, according to Mirantis. Tune batch size and learning rate together, and watch for memory fragmentation.<\/p>\n<\/div>\n<\/div>\n<\/section>\n<div class=\"cta-button-container\" style=\"text-align: center;margin: 32px 0\">\n<p>    <a href=\"https:\/\/www.serverpronto.com\/\" class=\"cta-button\" style=\"display: inline-block;background-color: #2563eb;color: #ffffff;padding: 14px 32px;border-radius: 8px;text-decoration: none;font-weight: 600;font-size: 16px\">Build &amp; Price<\/a>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to optimize GPU for AI training in 2026. Cut training time, fix data pipeline bottlenecks, and boost utilization with proven steps. Start now.<\/p>\n","protected":false},"author":0,"featured_media":9814,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[409],"tags":[446,445,443,444,442],"class_list":["post-9811","post","type-post","status-publish","format-standard","has-post-thumbnail","category-blog","tag-batch-size-optimization","tag-data-pipeline-bottlenecks","tag-how-to-optimize-gpu-for-ai-training","tag-mixed-precision-training","tag-optimize-gpu"],"_links":{"self":[{"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/posts\/9811","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/comments?post=9811"}],"version-history":[{"count":1,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/posts\/9811\/revisions"}],"predecessor-version":[{"id":9813,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/posts\/9811\/revisions\/9813"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/media\/9814"}],"wp:attachment":[{"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/media?parent=9811"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/categories?post=9811"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.serverpronto.com\/spu\/wp-json\/wp\/v2\/tags?post=9811"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}