{"id":3376,"date":"2026-05-25T22:20:03","date_gmt":"2026-05-25T20:20:03","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/capacity-comparison-tensorflow-vs-pytorch-vs-neural-designer\/"},"modified":"2026-08-25T14:03:02","modified_gmt":"2026-08-25T12:03:02","slug":"capacity-comparison-tensorflow-vs-pytorch-vs-neural-designer","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/capacity-comparison-tensorflow-vs-pytorch-vs-neural-designer\/","title":{"rendered":"Data capacity of TensorFlow, PyTorch, and Neural Designer"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndb-lead a{color:#2d799f;font-weight:600}.ndb-lead img{display:block;margin:16px auto 4px;max-width:min(440px,100%)!important;width:auto!important;height:auto;border-radius:12px;box-shadow:0 12px 28px rgba(0,18,51,.12)}.ndb-highlight{margin:0 0 36px;padding:28px 34px;border-radius:18px;background:linear-gradient(135deg,#56a1c8 0%,#245e80 100%);color:#fff;box-shadow:0 16px 34px rgba(30,83,116,.22)}.ndb-highlight p{margin:0;color:#fff;font-size:20px;line-height:1.5;font-weight:400}.ndb-highlight b{font-weight:800}.ndb-toc{list-style:none;display:flex;flex-wrap:wrap;gap:10px;justify-content:center;margin:0 0 42px;padding:0}.ndb-toc a{padding:10px 18px;background:#f2f2f2;border-radius:22px;color:#12354b!important;font-size:14px;font-weight:600;box-shadow:-6px -6px 12px rgba(255,255,255,.9),6px 6px 12px rgba(30,83,116,.10)}.ndb-toc a:hover{color:#2d799f!important}.ndb-card{margin:0 0 42px;padding:0;background:transparent;border-radius:0;box-shadow:none;scroll-margin-top:90px}.ndb-card h2{margin:0 0 20px;padding-bottom:12px;color:#001233;font-size:24px;font-weight:700;position:relative;border-bottom:1px solid #dbe5ec}.ndb-card h2:after{content:\"\";position:absolute;left:0;bottom:-1px;width:62px;height:3px;background:#56a1c8;border-radius:2px}.ndb-card p{margin:0 0 14px;font-size:16.5px;line-height:1.62;color:#33424f}.ndb-card a{color:#2d799f;font-weight:500}.ndb-card ul{margin:0 0 14px;padding-left:22px}.ndb-card li{margin:5px 0;font-size:16px;line-height:1.5;color:#33424f}.ndb-card img:not([src$=\".svg\"]):not([data-src$=\".svg\"]){display:block;width:auto;max-width:min(560px,100%);height:auto;margin:22px auto;border-radius:12px;box-shadow:0 12px 28px rgba(0,18,51,.12)}.ndb-card th img[src$=\".svg\"],.ndb-card th img[data-src$=\".svg\"],.ndb-card img[src$=\".svg\"],.ndb-card img[data-src$=\".svg\"]{display:inline-block;max-width:24px;height:auto;margin:0 6px -4px 0;box-shadow:none}.ndb-card table{border-collapse:separate;border-spacing:0;width:100%;max-width:100%;margin:22px 0;font-size:15px;background:#fbfcfd;border-radius:12px;overflow:hidden;box-shadow:0 10px 24px rgba(0,18,51,.08)}.ndb-card th,.ndb-card td{padding:11px 16px;border-bottom:1px solid #e6ecf0;text-align:left;vertical-align:top}.ndb-card thead th{background:#12354b;color:#fff;font-weight:600;text-align:center}.ndb-card tbody th{background:#e9f1f6;color:#12354b;font-weight:600}.ndb-card td{color:#33424f}.ndb-card table ul{margin:0;padding-left:18px}.ndb-card table li{font-size:14px}.ndb-card pre{margin:22px 0;padding:22px 24px;background:#0b1830!important;color:#e6eef5!important;border-radius:14px;overflow-x:auto;font-family:Consolas,Menlo,monospace;font-size:12.5px;line-height:1.5;white-space:pre}.ndb-card--accent{background:transparent}@media(max-width:820px){.ndb-card{padding:0}.ndb-card h2{font-size:21px}.ndb-toc{gap:8px}}@media(max-width:640px){.ndb{padding:12px 14px}}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><div class=\"ndb-lead\"><\/p>\n<p>In this post, we compare the load capacity of three machine learning platforms: <a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a>, <a href=\"https:\/\/pytorch.org\/\">PyTorch<\/a> and <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>\u00a0for an approximation benchmark. Capacity means the maximum amount of data a computer program can analyze in data science and machine learning platforms.<\/p>\n<p>These platforms are developed by\u00a0<a href=\"https:\/\/research.google\/teams\/brain\/\">Google<\/a>,\u00a0<a href=\"https:\/\/ai.facebook.com\/research\/\">Facebook<\/a> and\u00a0<a href=\"https:\/\/www.artelnics.com\/\">Artelnics<\/a>, respectively.<img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/capacity-approximation-results.webp\" alt=\"A graph comparing the data capacity of TensorFlow, Pytorch and Neural Designer. It can be seen that Neural Designer is able to load a dataset x1.8.\" width=\"486\" height=\"299\" \/><\/p>\n<p data-start=\"50\" data-end=\"154\">In this article, we outline all the steps required to reproduce the results using Neural Designer (<a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">download<\/a>).<\/p>\n<\/div><div class=\"ndb-highlight\"><p>As we will see, <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> is able to load a dataset <b>x1.8<\/b> larger than <a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a> and <a href=\"https:\/\/pytorch.org\/\">PyTorch<\/a>.<\/p>\n<\/div><ul class=\"ndb-toc\"><li><a href=\"#Introduction\">Introduction<\/a><\/li><li><a href=\"#BenchmarkApplication\">Benchmark application<\/a><\/li><li><a href=\"#ReferenceComputer\">Reference computer<\/a><\/li><li><a href=\"#Results\">Results<\/a><\/li><li><a href=\"#Conclusions\">Conclusions<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"Introduction\"><h2>Introduction<\/h2><p>The maximum amount of data a tool can analyze depends on different factors. Some of the most important ones are the programming language in which it is written and how the memory usage works internally.<\/p>\n<p>The following table summarizes the technical features of these tools that might impact their memory usage.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00a0<\/th>\n<th>TensorFlow<\/th>\n<th>PyTorch<\/th>\n<th>Neural Designer<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Written in<\/th>\n<td>C++, CUDA, Python<\/td>\n<td>C++, CUDA, Python<\/td>\n<td>C++, CUDA<\/td>\n<\/tr>\n<tr>\n<th>Interface<\/th>\n<td>Python<\/td>\n<td>Python<\/td>\n<td>Graphical User Interface<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Even though C++ is at the core of the three platforms, their interfaces are different. The most common use of TensorFlow and PyTorch is through a <a href=\"https:\/\/www.python.org\/\">Python<\/a> API. On the other hand, Neural Designer uses a <a href=\"https:\/\/www.cplusplus.com\/\">C++<\/a> GUI.<\/p>\n<p>As we will see, an application with a Python interface results in higher memory consumption, which means a lower capacity to load the data.<\/p>\n<\/div><div class=\"ndb-card\" id=\"BenchmarkApplication\"><h2>Benchmark application<\/h2><p>To test the capacity of the three platforms, we will try to load different <a href=\"https:\/\/www.neuraldesigner.com\/blog\/the-rosenbrock-benchmark-for-machine-learning\/\">Rosenbrock<\/a> datafiles, fixing the number of variables and changing the number of samples. The following table shows the correspondence between the size and the number of Rosenbrock samples.<\/p>\n<table>\n<thead>\n<tr>\n<th>Filename<\/th>\n<th>Floating points number (x10<sup>9<\/sup>)<\/th>\n<th>Samples number<\/th>\n<th>Size (Gb)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_1000000_samples.csv<\/td>\n<td style=\"text-align: center;\">1<\/td>\n<td style=\"text-align: center;\">1000000<\/td>\n<td style=\"text-align: right;\">22<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_2000000_samples.csv<\/td>\n<td style=\"text-align: center;\">2<\/td>\n<td style=\"text-align: center;\">2000000<\/td>\n<td style=\"text-align: right;\">44<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_3000000_samples.csv<\/td>\n<td style=\"text-align: center;\">3<\/td>\n<td style=\"text-align: center;\">3000000<\/td>\n<td style=\"text-align: right;\">65<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_4000000_samples.csv<\/td>\n<td style=\"text-align: center;\">4<\/td>\n<td style=\"text-align: center;\">4000000<\/td>\n<td style=\"text-align: right;\">86<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_5000000_samples.csv<\/td>\n<td style=\"text-align: center;\">5<\/td>\n<td style=\"text-align: center;\">5000000<\/td>\n<td style=\"text-align: right;\">107<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_6000000_samples.csv<\/td>\n<td style=\"text-align: center;\">6<\/td>\n<td style=\"text-align: center;\">6000000<\/td>\n<td style=\"text-align: right;\">128<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_7000000_samples.csv<\/td>\n<td style=\"text-align: center;\">7<\/td>\n<td style=\"text-align: center;\">7000000<\/td>\n<td style=\"text-align: right;\">149<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_8000000_samples.csv<\/td>\n<td style=\"text-align: center;\">8<\/td>\n<td style=\"text-align: center;\">8000000<\/td>\n<td style=\"text-align: right;\">171<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_9000000_samples.csv<\/td>\n<td style=\"text-align: center;\">9<\/td>\n<td style=\"text-align: center;\">9000000<\/td>\n<td style=\"text-align: right;\">192<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Rosenbrock_1000_variables_10000000_samples.csv<\/td>\n<td style=\"text-align: center;\">10<\/td>\n<td style=\"text-align: center;\">10000000<\/td>\n<td style=\"text-align: right;\">213<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>To create these files check this article: <a href=\"https:\/\/www.neuraldesigner.com\/blog\/the-rosenbrock-benchmark-for-machine-learning\/\"> The Rosenbrock Dataset Suite for benchmarking approximation algorithms and platforms<\/a>.<\/p>\n<p>The number of samples is the only parameter we change to perform the comparison tests. The other parameters are constant. The next picture shows the benchmarks set up.<\/p>\n<table>\n<tbody>\n<tr>\n<th>Data set<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/data_set.svg\"  alt=\"Data Set\"\/><\/th>\n<td>\n<ul>\n<li>Benchmark: Rosenbrock<\/li>\n<li>Inputs number: 1000<\/li>\n<li>Targets number: 1<\/li>\n<li>Samples number: Table<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Neural network<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural_network.svg\"  alt=\"Neural Network\"\/><\/th>\n<td>\n<ul>\n<li>Layers number: 2<\/li>\n<li>Layer 1:\n<ul style=\"list-style-type: none;\">\n<li>-Type: Perceptron (Dense)<\/li>\n<li>-Inputs number: 1000<\/li>\n<li>-Neurons number: 1000<\/li>\n<li>-Activation function: Hyperbolic tangent (tanh)<\/li>\n<\/ul>\n<\/li>\n<li>Layer 2:\n<ul style=\"list-style-type: none;\">\n<li>-Type: Perceptron (Dense)<\/li>\n<li>-Inputs number: 1000<\/li>\n<li>-Neurons number: 1<\/li>\n<li>-Activation function: Linear<\/li>\n<\/ul>\n<\/li>\n<li>Initialization: Random uniform [-1,1]<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr>\n<th>Training strategy<br \/><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/training_strategy.svg\"  alt=\"Training Strategy\"\/><\/th>\n<td>\n<ul>\n<li>Loss index:\n<ul style=\"list-style-type: none;\">\n<li>-Error: Mean Squared Error (MSE)<\/li>\n<li>-Regularization: None<\/li>\n<\/ul>\n<\/li>\n<li>Optimization algorithm:\n<ul style=\"list-style-type: none;\">\n<li>-Algorithm: Adaptive Moment Estimation (Adam)<\/li>\n<li>-Batch size: 1000<\/li>\n<li>-Maximum epochs: 1000<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>We run the above benchmark for each platform (TensorFlow, PyTorch, and Neural Designer), increasing samples until the memory crashes. We consider a successful test if it can load the CSV file and train the neural network.<\/p>\n<\/div><div class=\"ndb-card\" id=\"ReferenceComputer\"><h2>Reference computer<\/h2><p>The next step involves choosing the computer to train the neural networks with TensorFlow, PyTorch, and Neural Designer. For a capacity test, the most crucial feature of the computer is its memory.<\/p>\n<p>We have made all calculations on an Amazon Web Services instance (<a href=\"https:\/\/aws.amazon.com\/\">AWS<\/a>). In particular, we have chosen the <a href=\"https:\/\/aws.amazon.com\/es\/ec2\/instance-types\/r5\/\"> r5.large.<\/a> so that you can reproduce the results easily. The next table lists some basic information about the computer used here.<\/p>\n<table>\n<thead>\n<tr>\n<th>Operating system<\/th>\n<th>Windows 10 Enterprise<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Processor<\/td>\n<td>Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz<\/td>\n<\/tr>\n<tr>\n<td>Installed memory (RAM):<\/td>\n<td>16.0 GB<\/td>\n<\/tr>\n<tr>\n<td>System type:<\/td>\n<td>64-bit Operating system, x64 based processor<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Once the computer is chosen, we install TensorFlow (2.1.0), PyTorch (1.7.0) and Neural Designer(5.0.0) on it.<\/p>\n<\/div><div class=\"ndb-card\" id=\"Results\"><h2>Results<\/h2><p>The last step is to run the benchmark application using TensorFlow, PyTorch, and Neural Designer. Then, we compare the capacity results provided by those platforms.<\/p>\n<p>The following table shows whether or not each platform can load the different data files. The blue check means that the platform can load it and the orange cross means that it is not able.<\/p>\n<table>\n<thead>\n<tr>\n<th>Floating points<br \/>number (x10<sup>9<\/sup>)<\/th>\n<th>TensorFlow<\/th>\n<th>PyTorch<\/th>\n<th>Neural Designer<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: center;\">1<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">2<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">3<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">4<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">5<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">6<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">7<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">8<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">9<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/check-blue.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">10<\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<td style=\"text-align: right;\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/cross-orange.svg\"  alt=\"\"\/><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>As we can see, the maximum capacity of both TensorFlow and PyTorch is 5 x 10<sup>9<\/sup> data and the maximum capacity of Neural Designer is 9 x 10<sup>9<\/sup> data.<\/p>\n<p>These results can also be depicted graphically.<\/p>\n<p><img decoding=\"async\" style=\"width: 800px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/capacity-approximation-results.webp\"  alt=\"Capacity Approximation Results\"\/><\/p>\n<p>From these results, we can conclude that <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a> is able to load a dataset <b>x1.8<\/b> larger than <a href=\"https:\/\/tensorflow.org\/\">TensorFlow<\/a> and <a href=\"https:\/\/pytorch.org\/\">PyTorch<\/a>.<\/p>\n<p>The following picture shows that Neural Designer can train a neural network with 9 billion data points in a 16GB RAM computer.<\/p>\n<p><img decoding=\"async\" style=\"width: 1200px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/neural-designer-load-capacity.webp\"  alt=\"Neural Designer Load Capacity\"\/><\/p>\n<p>The following picture shows how TensorFlow runs out of memory when trying to load a data file containing 6 billion data.<\/p>\n<p><img decoding=\"async\" style=\"width: 1200px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/tensorflow-capacity.webp\"  alt=\"Tensorflow Capacity\"\/><\/p>\n<p>As we can see, when the external module Python <a href=\"https:\/\/pandas.pydata.org\/\">pandas<\/a> in TensorFlow tries to load 6 billion data, the platform crashes due to lack of RAM. TensorFlow&#8217;s maximum capacity is 5 billion data.<\/p>\n<p>The following picture shows how PyTorch also runs out of memory when loading a data file containing 6 billion data.<\/p>\n<p><img decoding=\"async\" style=\"width: 1200px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/pytorch-capacity.webp\"  alt=\"Pytorch Capacity\"\/><\/p>\n<p>Again, when the external module Python <a href=\"https:\/\/pandas.pydata.org\/\">pandas<\/a> in PyTorch tries to load 6 billion data, it crashes. PyTorch&#8217;s maximum capacity is 5 billion data.<\/p><\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"Conclusions\"><h2>Conclusions<\/h2><p>The maximum capacity in both TensorFlow and PyTorch is 5 billion data, and the maximum capacity in Neural Designer is 9 billion.<\/p>\n<p>This difference is because TensorFlow and PyTorch use an external module (Python pandas) to load data. In contrast, Neural Designer uses its function to load data, which offers an advantage.<\/p>\n<p>Indeed, Python is a high-level programming language. However, this causes a lower capacity for Python tools to load data.<\/p>\n<p>To reproduce these results, <a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">download<\/a>\u00a0Neural Designer and follow the steps described in this article.<\/p>\n<\/div><\/div><\/div>","protected":false},"author":1,"featured_media":2492,"template":"","categories":[],"tags":[37],"class_list":["post-3376","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-platforms"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data capacity of TensorFlow, PyTorch, and Neural Designer<\/title>\n<meta name=\"description\" content=\"Comparison of the data capacity of three machine learning platforms: TensorFlow, PyTorch and Neural Designer for an approximation benchmark.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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