{"id":4836,"date":"2026-07-18T21:33:09","date_gmt":"2026-07-18T16:03:09","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/"},"modified":"2026-07-18T21:33:09","modified_gmt":"2026-07-18T16:03:09","slug":"top-artificial-intelligence-best-practices-for-success-3","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/","title":{"rendered":"Top Artificial Intelligence Best Practices for Success"},"content":{"rendered":"<p><code>[2023-10-24 03:14:22.891] ERROR: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 80.00 GiB total capacity; 64.22 GiB already allocated; 1.12 GiB free; 72.45 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF<\/code><br \/>\n<code>[2023-10-24 03:14:25.102] WARNING: GPU 0: Thermal Throttling Active. Temp: 89C. Clock: 420MHz (Target: 1410MHz). Power Draw: 685W \/ 700W.<\/code><br \/>\n<code>[2023-10-24 03:14:25.105] FATAL: Kernel panic - not syncing: Watchdog detected hard LOCKUP on cpu 48<\/code><\/p>\n<p>I don&#8217;t care about your &#8216;neural&#8217; metaphors; if your weights don&#8217;t fit in the L3 cache, you&#8217;re just heating the room for nothing. <\/p>\n<p>You software types come in here with your &#8220;artificial intelligence&#8221; dreams and your Python scripts, thinking the hardware is some infinite, ethereal plane where logic just happens. It isn&#8217;t. It\u2019s a collection of silicon gates screaming under the pressure of your inefficient abstractions. You treat a GPU like a magic box, but it\u2019s just a very fast, very stupid array of arithmetic logic units (ALUs) that are currently choking on your garbage memory management. You\u2019ve spent the last decade wrapping everything in layers of &#8220;convenience&#8221;\u2014Docker on top of a VM on top of a hypervisor on top of a kernel that\u2019s trying to manage a memory bus it barely understands. Every layer is a tax. Every abstraction is a latency penalty. And now you\u2019re trying to run &#8220;artificial intelligence&#8221; models that require more bandwidth than the laws of physics want to give you, and you\u2019re wondering why the rack is melting.<\/p>\n<p>I\u2019ve been in these windowless rooms since we were hand-soldering serial ports, and I\u2019m telling you: the party is over. The &#8220;free lunch&#8221; of Moore\u2019s Law is dead, and your software is the bloated corpse. If you want to deploy &#8220;artificial intelligence&#8221; without burning down the data center or going bankrupt on H100 compute credits, you need to stop thinking like a mathematician and start thinking like an electron.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a5bb9f673f6a\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a5bb9f673f6a\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_1_Respect_the_VRAM_or_Get_Out_of_My_Data_Center\" >DIRECTIVE 1: Respect the VRAM or Get Out of My Data Center<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_2_Purge_the_Dependency_Hell_of_Python_3114\" >DIRECTIVE 2: Purge the Dependency Hell of Python 3.11.4<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_3_Quantize_or_Suffer_the_Thermal_Throttling\" >DIRECTIVE 3: Quantize or Suffer the Thermal Throttling<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_4_The_PCIe_Gen5_Bus_is_a_Bottleneck_Stop_Pretending_Otherwise\" >DIRECTIVE 4: The PCIe Gen5 Bus is a Bottleneck, Stop Pretending Otherwise<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_5_The_Lie_of_Infinite_Scalability\" >DIRECTIVE 5: The Lie of Infinite Scalability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#DIRECTIVE_6_Stop_Treating_%E2%80%9CArtificial_Intelligence%E2%80%9D_Like_Magic\" >DIRECTIVE 6: Stop Treating &#8220;Artificial Intelligence&#8221; Like Magic<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#THE_TECHNICAL_MINUTIAE_OF_SURVIVAL\" >THE TECHNICAL MINUTIAE OF SURVIVAL<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#THE_REALITY_OF_THE_H100_TRANSFORMER_ENGINE\" >THE REALITY OF THE H100 TRANSFORMER ENGINE<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#PRUNING_SPARSITY_AND_THE_BITTER_LESSON\" >PRUNING, SPARSITY, AND THE BITTER LESSON<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#FINAL_DIRECTIVE_MONITOR_THE_BARE_METAL\" >FINAL DIRECTIVE: MONITOR THE BARE METAL<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/#Related_Articles\" >Related Articles<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_1_Respect_the_VRAM_or_Get_Out_of_My_Data_Center\"><\/span>DIRECTIVE 1: Respect the VRAM or Get Out of My Data Center<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You see an NVIDIA H100 with 80GB of HBM3 and you think, &#8220;Great, I can fit a 70B parameter model in there.&#8221; You\u2019re wrong. You\u2019re catastrophically wrong. You haven&#8217;t accounted for the KV cache, the activation buffers, or the overhead of the CUDA context itself. When you\u2019re running &#8220;artificial intelligence&#8221; at scale, the memory wall isn&#8217;t a suggestion; it\u2019s a physical barrier.<\/p>\n<p>An H100 SXM5 has a memory bandwidth of about 3.35 TB\/s. That sounds fast until you realize your model is doing trillions of operations per second. If your data isn&#8217;t sitting exactly where the Tensor Cores can grab it, those cores sit idle. An idle Tensor Core is a $30,000 paperweight. You\u2019re wasting cycles waiting for the bus. <\/p>\n<p>Stop using FP32. If I see a single FP32 weight in a production inference pipeline, I will personally pull the power cord on your rack. We are in the era of FP8 and INT4. If your &#8220;artificial intelligence&#8221; can&#8217;t handle a little quantization noise, your architecture is brittle and your training data is garbage. Use the Transformer Engine in the H100. It\u2019s there for a reason. It handles the dynamic scaling between FP8 and FP16 so you don&#8217;t have to, but you\u2019re too busy writing &#8220;clean code&#8221; to actually read the hardware manual.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_2_Purge_the_Dependency_Hell_of_Python_3114\"><\/span>DIRECTIVE 2: Purge the Dependency Hell of Python 3.11.4<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Your <code>requirements.txt<\/code> is a suicide note. You\u2019re pulling in 400 dependencies to do a simple matrix multiplication. Look at this mess I found on the dev server yesterday:<\/p>\n<pre class=\"codehilite\"><code class=\"language-text\"># BROKEN DEPENDENCY HELL - DO NOT REPLICATE\ntorch==2.1.0+cu121\nnvidia-cuda-runtime-cu12==12.2.140\nnumpy==1.24.3\npandas==2.0.3\ntransformers==4.31.0\naccelerate==0.21.0\nbitsandbytes==0.41.1\n# Conflict: bitsandbytes requires a specific CUDA version \n# that conflicts with the torch-bundled runtime.\n# Result: Segmentation fault (core dumped)\n<\/code><\/pre>\n<p>You\u2019re running Python 3.11.4. Fine. It\u2019s faster than 3.10, but it\u2019s still Python. The Global Interpreter Lock (GIL) is still there, lurking like a ghost in the machine, making sure your multi-core CPU spends 90% of its time waiting for a mutex. When you\u2019re deploying &#8220;artificial intelligence,&#8221; you shouldn&#8217;t even be in Python for the hot path. Python is for the configuration; C++ and Triton are for the execution.<\/p>\n<p>And stop using <code>pip install<\/code>. If you aren&#8217;t pinning your versions to the exact hash, you aren&#8217;t an engineer; you\u2019re a gambler. I\u2019ve seen production clusters go down because a sub-dependency of a sub-dependency updated from 1.0.2 to 1.0.3 and changed the way it handles memory pinning. In CUDA Toolkit 12.2, the way memory is mapped has changed. If your library is expecting the 11.8 behavior, you\u2019re going to get a silent corruption that will make your &#8220;artificial intelligence&#8221; output gibberish for three weeks before you even notice.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_3_Quantize_or_Suffer_the_Thermal_Throttling\"><\/span>DIRECTIVE 3: Quantize or Suffer the Thermal Throttling<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Let\u2019s talk about the 700W TDP of an H100. That is not a peak; that is a sustained reality when you\u2019re pushing &#8220;artificial intelligence&#8221; workloads. If you have eight of those in a 4U chassis, you are trying to dissipate 5.6 kilowatts of heat from a box the size of a microwave. <\/p>\n<p>If you run your models at full precision, you are moving twice as many bits as necessary across the memory bus. Moving bits costs energy. Energy creates heat. Heat triggers the thermal controllers. When that GPU hits 83C, the clock speed drops. When it hits 89C, it throttles to the floor. Your &#8220;high-performance&#8221; model is now running slower than a 2015 GTX 980 because you were too lazy to implement 4-bit quantization.<\/p>\n<p>Use NF4 (NormalFloat 4). It\u2019s designed for the distribution of weights in &#8220;artificial intelligence&#8221; models. It\u2019s not perfect, but it\u2019s better than the alternative: a rack that sounds like a jet engine and performs like a calculator. And don&#8217;t give me that &#8220;accuracy loss&#8221; excuse. If a 4-bit quantization ruins your model, your model was overfitted to begin with. Prune the dead weights. If a weight is less than 0.001, it\u2019s noise. Kill it. Your L3 cache will thank you.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_4_The_PCIe_Gen5_Bus_is_a_Bottleneck_Stop_Pretending_Otherwise\"><\/span>DIRECTIVE 4: The PCIe Gen5 Bus is a Bottleneck, Stop Pretending Otherwise<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You think because you have a PCIe Gen5 slot, you have infinite bandwidth. You don&#8217;t. You have 64 GB\/s. That is a trickle compared to the 900 GB\/s of NVLink. If you are building &#8220;artificial intelligence&#8221; systems that rely on moving large tensors back and forth between the CPU and GPU, you have already failed.<\/p>\n<p>The &#8220;artificial intelligence&#8221; should live on the GPU. It should stay on the GPU. The CPU is just a glorified traffic cop. I see people using <code>tensor.to('cpu')<\/code> in the middle of a loop and I want to weep. Do you know what that does? It stalls the entire pipeline. It flushes the command queue. It forces a synchronization point. It\u2019s the equivalent of stopping a freight train to check if a single passenger has their ticket.<\/p>\n<p>If you must move data, use DMA (Direct Memory Access). Use pinned memory. Stop letting the Linux kernel\u2019s virtual memory manager decide when to swap your tensors to disk. Set <code>hugepages<\/code>. If you aren&#8217;t using <code>mmap<\/code> for your model weights, you\u2019re letting the OS bloat your latency with page faults.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_5_The_Lie_of_Infinite_Scalability\"><\/span>DIRECTIVE 5: The Lie of Infinite Scalability<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Marketing people love to talk about &#8220;seamlessly&#8221; scaling &#8220;artificial intelligence&#8221; in the cloud. There is nothing &#8220;seamless&#8221; about it. Scaling means more cables, more switches, and more points of failure. When you scale to 1,000 GPUs, you aren&#8217;t 1,000 times faster. You\u2019re lucky if you\u2019re 600 times faster, because the rest of that compute is being eaten by the collective overhead of NCCL (NVIDIA Collective Communications Library) trying to keep all those weights in sync.<\/p>\n<p>The &#8220;artificial intelligence&#8221; hype ignores the physical reality of the speed of light. Electrons can only move so fast through a copper trace. When you\u2019re doing all-reduce operations across a cluster, the latency of your InfiniBand switch becomes the heartbeat of your system. If one cable is slightly crimped, if one transceiver is overheating, your entire $100 million cluster slows down to the speed of that one failing component.<\/p>\n<p>You want scalability? Optimize your kernels. Use Triton to write custom fused kernels that combine the activation function with the matrix multiplication. This reduces the number of times you have to write to VRAM. Every VRAM write is a power-hungry, slow operation. Fusing kernels is the only way to beat the memory wall. But that requires actual engineering, not just importing a library from GitHub.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"DIRECTIVE_6_Stop_Treating_%E2%80%9CArtificial_Intelligence%E2%80%9D_Like_Magic\"><\/span>DIRECTIVE 6: Stop Treating &#8220;Artificial Intelligence&#8221; Like Magic<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>It\u2019s just math. It\u2019s just a massive amount of dot products. There is no &#8220;soul&#8221; in the machine, just a bunch of floating-point numbers that are currently being handled with the grace of a sledgehammer. <\/p>\n<p>The current trend of &#8220;artificial intelligence&#8221; development is to throw more hardware at the problem. &#8220;Just add more H100s,&#8221; they say. That is the philosophy of the incompetent. A real architect makes the model smaller, faster, and more efficient. They look at the memory access patterns. They look at the bank conflicts in the shared memory of the GPU. They understand that a warp of 32 threads should never diverge, or you\u2019re wasting 31 threads of execution.<\/p>\n<p>We are building a &#8220;Field Manual for the Resistance&#8221; because the current path is unsustainable. We are reaching the limits of what air cooling can handle. We are reaching the limits of what the power grid can provide to a single city block. If we don&#8217;t start respecting the silicon, the silicon is going to stop respecting us.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"THE_TECHNICAL_MINUTIAE_OF_SURVIVAL\"><\/span>THE TECHNICAL MINUTIAE OF SURVIVAL<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Let\u2019s get into the weeds, because that\u2019s where the failures happen. You\u2019re using CUDA 12.2. Do you even know what the new memory allocator does? It tries to be &#8220;smart&#8221; by caching allocations. But in &#8220;artificial intelligence&#8221; workloads with variable sequence lengths, this leads to massive fragmentation. You\u2019ll see 72GB &#8220;reserved&#8221; but only 40GB &#8220;allocated,&#8221; and your script will crash with an OOM error because it can&#8217;t find a contiguous 4GB block for the next attention head.<\/p>\n<p>You need to set <code>PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512<\/code>. Or better yet, write your own memory pool. Stop relying on PyTorch to hold your hand. It\u2019s a general-purpose tool, and &#8220;artificial intelligence&#8221; at scale is not a general-purpose problem.<\/p>\n<p>And what about the &#8220;garbage&#8221; that is the Python interpreter? Every time you create a new tensor object, Python has to allocate a small object on the heap, increment a reference count, and eventually garbage collect it. When you\u2019re doing this millions of times a second, the overhead is non-trivial. I\u2019ve seen &#8220;artificial intelligence&#8221; benchmarks where 15% of the wall-clock time was spent in Python\u2019s <code>gc.collect()<\/code>. Turn off the garbage collector during the inference loop. Manually manage your memory. It\u2019s what we did in the 90s, and it\u2019s what you need to do now if you want to survive the &#8220;artificial intelligence&#8221; gold rush without going broke.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"THE_REALITY_OF_THE_H100_TRANSFORMER_ENGINE\"><\/span>THE REALITY OF THE H100 TRANSFORMER ENGINE<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The H100 isn&#8217;t just a faster A100. It has a dedicated hardware block for handling the scaling factors of FP8. If you aren&#8217;t using the <code>transformer_engine<\/code> library from NVIDIA, you are leaving 3x performance on the table. But of course, that library requires a specific version of <code>ninja<\/code> and a specific version of <code>g++<\/code>, and your environment is a mess of conflicting versions. <\/p>\n<p>Here is what your environment should look like, if you had any discipline:<br \/>\n&#8211; <strong>OS:<\/strong> Ubuntu 22.04.3 LTS (Kernel 5.15.0-84-generic)<br \/>\n&#8211; <strong>Compiler:<\/strong> GCC 11.4.0<br \/>\n&#8211; <strong>CUDA:<\/strong> 12.2 Update 2<br \/>\n&#8211; <strong>Driver:<\/strong> 535.104.05<br \/>\n&#8211; <strong>Python:<\/strong> 3.11.4 (compiled from source with <code>--enable-optimizations<\/code>)<\/p>\n<p>If you\u2019re running on a &#8220;Data Science&#8221; AMI you found in the AWS marketplace, you\u2019ve already lost. Those images are packed with bloatware that steals CPU cycles and pollutes your <code>LD_LIBRARY_PATH<\/code>. Build your own stack. Know every library that is linked to your binary.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"PRUNING_SPARSITY_AND_THE_BITTER_LESSON\"><\/span>PRUNING, SPARSITY, AND THE BITTER LESSON<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Rich Sutton wrote about &#8220;The Bitter Lesson&#8221;\u2014that general-purpose methods that leverage compute are the most effective. He was right, but he forgot to mention that compute isn&#8217;t free. The next stage of &#8220;artificial intelligence&#8221; isn&#8217;t &#8220;bigger models&#8221;; it\u2019s &#8220;smarter hardware utilization.&#8221;<\/p>\n<p>NVIDIA\u2019s Ampere and Hopper architectures support 2:4 structured sparsity. This means for every four weights, two must be zero. If you do this, the Tensor Cores can double their throughput. Are you using this? No. You\u2019re too busy chasing the next leaderboard spot to bother with a pruning schedule during training. You\u2019d rather pay for 2x the GPUs than do 1x the engineering.<\/p>\n<p>This is why the &#8220;Resistance&#8221; exists. We are the ones who have to keep these machines running when the &#8220;artificial intelligence&#8221; hype cycle hits the wall of physical reality. We are the ones who have to explain to the CFO why the electricity bill for the data center is higher than the revenue from the product.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FINAL_DIRECTIVE_MONITOR_THE_BARE_METAL\"><\/span>FINAL DIRECTIVE: MONITOR THE BARE METAL<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Stop looking at your &#8220;Weights &amp; Biases&#8221; dashboard and start looking at <code>nvidia-smi dmon<\/code>. Watch the <code>pwr<\/code> and <code>temp<\/code> columns. Watch the <code>mclk<\/code> (memory clock) and <code>pclk<\/code> (processor clock). If you see the <code>pclk<\/code> dropping while the <code>util<\/code> is at 100%, you are thermal throttling. You are failing.<\/p>\n<p>&#8220;Artificial intelligence&#8221; is a resource management game. The weights are your inventory, the VRAM is your warehouse, and the PCIe bus is your delivery truck. If you try to cram a million-ton shipment into a pickup truck, it doesn&#8217;t matter how &#8220;intelligent&#8221; your routing algorithm is; the truck is going to break an axle.<\/p>\n<p>Go back to your code. Strip out the abstractions. Look at the memory layouts. Check your strides. Ensure your tensors are contiguous in memory before you pass them to a CUDA kernel. If you don&#8217;t know what a &#8220;stride&#8221; is in the context of a multi-dimensional array, you shouldn&#8217;t be allowed to call yourself an &#8220;artificial intelligence&#8221; engineer. You\u2019re just a script kiddie playing with expensive toys.<\/p>\n<p>The silicon doesn&#8217;t care about your &#8220;neural&#8221; metaphors. It only cares about the electrons. Respect the electrons, or they will turn into heat and destroy everything you\u2019ve built.<\/p>\n<p><strong>END OF DEBRIEF.<\/strong><br \/>\n<strong>STATUS: THERMAL CRITICAL.<\/strong><br \/>\n<strong>ACTION: SHUTTING DOWN NON-ESSENTIAL ABSTRACTIONS.<\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Related_Articles\"><\/span>Related Articles<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Explore more insights and best practices:<\/p>\n<ul>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/machine-learning-best-practices-a-guide-to-success-2\/\">Machine Learning Best Practices A Guide To Success 2<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/what-is-kubernetes-a-simple-guide-to-k8s-orchestration\/\">What Is Kubernetes A Simple Guide To K8S Orchestration<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-2\/\">Top Artificial Intelligence Best Practices For Success 2<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>[2023-10-24 03:14:22.891] ERROR: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 80.00 GiB total capacity; 64.22 GiB already allocated; 1.12 GiB free; 72.45 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF [2023-10-24 03:14:25.102] &#8230; <a title=\"Top Artificial Intelligence Best Practices for Success\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/\" aria-label=\"Read more  on Top Artificial Intelligence Best Practices for Success\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4836","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Top Artificial Intelligence Best Practices for Success - ITSupportWale<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsupportwale.com\/blog\/top-artificial-intelligence-best-practices-for-success-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top Artificial Intelligence Best Practices for Success - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"[2023-10-24 03:14:22.891] ERROR: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 12.50 GiB (GPU 0; 80.00 GiB total capacity; 64.22 GiB already allocated; 1.12 GiB free; 72.45 GiB reserved in total by PyTorch) If reserved memory is &gt;&gt; allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF [2023-10-24 03:14:25.102] ... 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