{"id":4758,"date":"2026-04-12T21:14:44","date_gmt":"2026-04-12T15:44:44","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/artificial-intelligence-news-guide\/"},"modified":"2026-04-12T21:14:44","modified_gmt":"2026-04-12T15:44:44","slug":"artificial-intelligence-news-guide","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/artificial-intelligence-news-guide\/","title":{"rendered":"artificial intelligence news &#8211; Guide"},"content":{"rendered":"<p>text<br \/>\n[ 2592.148203] NVRM: GPU at PCI:0000:01:00: GPU-8d7f4a2b-3c1e-4f5d-9a8b-7c6d5e4f3a2b<br \/>\n[ 2592.148210] NVRM: Xid (PCI:0000:01:00): 31, GPU memory page fault in isolation<br \/>\n[ 2592.148215] nvidia-nvlink: Unregistered the Nvlink Core, major device number 234<br \/>\n[ 2592.148222] NVRM: os_schedule: Attempted to yield the CPU while holding a spinlock!<br \/>\n[ 2592.148230] BUG: kernel NULL pointer dereference, address: 0000000000000000<br \/>\n[ 2592.148235] #PF: supervisor read access in kernel mode<br \/>\n[ 2592.148240] #PF: error_code(0x0000) &#8211; not-present page<br \/>\n[ 2592.148245] PGD 0 P4D 0<br \/>\n[ 2592.148250] Oops: 0000 [#1] PREEMPT SMP NOPTI<br \/>\n[ 2592.148255] CPU: 14 PID: 4021 Comm: python3.11 Tainted: P           OE      5.15.0-101-generic #111-Ubuntu<br \/>\n[ 2592.148260] Hardware name: Supermicro AS -4124GS-TNR\/H12DSG-O-MS, BIOS 2.4 08\/24\/2023<br \/>\n[ 2592.148265] RIP: 0010:nv_set_system_info+0x45\/0x120 [nvidia]<br \/>\n[ 2592.148270] Code: 48 8b 05 3d 2e 00 00 48 85 c0 74 0b 48 8b 40 10 48 85 c0 75 02 31 c0 48 89 45 d0 48 8b 45 d0 48 85 c0 0f 84 8e 00 00 00 48 8b 00 &lt;48&gt; 8b 10 48 89 55 c8 48 8b 45 c8 48 85 c0 0f 84 7a 00 00 00 48 8b<br \/>\n[ 2592.148275] RSP: 0018:ffffb1a2c4e3f8d0 EFLAGS: 00010246<br \/>\n[ 2592.148280] RAX: 0000000000000000 RBX: ffff9a2b4c5d6000 RCX: 0000000000000000<br \/>\n[ 2592.148285] RDX: 0000000000000000 RSI: ffff9a2b4c5d6000 RDI: ffff9a2b4c5d6000<br \/>\n[ 2592.148290] RBP: ffffb1a2c4e3f910 R08: 0000000000000000 R09: 0000000000000001<br \/>\n[ 2592.148295] R10: 0000000000000000 R11: 0000000000000000 R12: ffff9a2b4c5d6000<br \/>\n[ 2592.148300] R13: 0000000000000000 R14: 0000000000000000 R15: 0000000000000000<br \/>\n[ 2592.148305] FS:  00007f3e1a2b3c4d(0000) GS:ffff9a3a7f780000(0000) knlGS:0000000000000000<br \/>\n[ 2592.148310] CS:  0010 DS: 0000 ES: 0000 CR0: 0000000080050033<br \/>\n[ 2592.148315] CR2: 0000000000000000 CR3: 000000014c5d6000 CR4: 00000000003506e0<br \/>\n[ 2592.148320] Call Trace:<br \/>\n[ 2592.148325]  <TASK><br \/>\n[ 2592.148330]  ? show_regs+0x6d\/0x80<br \/>\n[ 2592.148335]  ? __die+0x24\/0x70<br \/>\n[ 2592.148340]  ? page_fault_oops+0x15a\/0x2d0<br \/>\n[ 2592.148345]  ? do_user_addr_fault+0x65\/0x6a0<br \/>\n[ 2592.148350]  ? exc_page_fault+0x77\/0x170<br \/>\n[ 2592.148355]  ? asm_exc_page_fault+0x27\/0x30<br \/>\n[ 2592.148360]  ? nv_set_system_info+0x45\/0x120 [nvidia]<br \/>\n[ 2592.148365]  nvidia_ioctl+0x5c2\/0xaf0 [nvidia]<br \/>\n[ 2592.148370]  ? __check_object_size+0x13f\/0x150<br \/>\n[ 2592.148375]  nvidia_frontend_ioctl+0x3a\/0x50 [nvidia]<br \/>\n[ 2592.148380]  __x64_sys_ioctl+0x91\/0xc0<br \/>\n[ 2592.148385]  do_syscall_64+0x5c\/0xc0<br \/>\n[ 2592.148390]  entry_SYSCALL_64_after_hwframe+0x61\/0xcb<br \/>\n[ 2592.148395]  <\/TASK><br \/>\n[ 2592.148400] &#8212;[ end trace 0000000000000000 ]&#8212;<\/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-69dbf45703e64\" 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-69dbf45703e64\"  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\/artificial-intelligence-news-guide\/#LOG_ENTRY_03_44_12_%E2%80%93_The_HBM3e_Mirage\" >[LOG_ENTRY_03:44:12] &#8211; The HBM3e Mirage<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_05_12_33_%E2%80%93_PDU_Meltdown_and_the_700W_Lie\" >[LOG_ENTRY_05:12:33] &#8211; PDU Meltdown and the 700W Lie<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_08_21_55_%E2%80%93_Python_3118_and_the_Bloatware_Stack\" >[LOG_ENTRY_08:21:55] &#8211; Python 3.11.8 and the Bloatware Stack<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_11_05_01_%E2%80%93_Thermal_Throttling_as_a_Business_Model\" >[LOG_ENTRY_11:05:01] &#8211; Thermal Throttling as a Business Model<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_14_30_19_%E2%80%93_The_Scaling_Law_Delusion\" >[LOG_ENTRY_14:30:19] &#8211; The Scaling Law Delusion<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_17_45_44_%E2%80%93_Driver_5505414_and_the_Death_of_Stability\" >[LOG_ENTRY_17:45:44] &#8211; Driver 550.54.14 and the Death of Stability<\/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\/artificial-intelligence-news-guide\/#LOG_ENTRY_21_10_02_%E2%80%93_The_Silicon_Graveyard\" >[LOG_ENTRY_21:10:02] &#8211; The Silicon Graveyard<\/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\/artificial-intelligence-news-guide\/#Related_Articles\" >Related Articles<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_03_44_12_%E2%80%93_The_HBM3e_Mirage\"><\/span>[LOG_ENTRY_03:44:12] &#8211; The HBM3e Mirage<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There it is. The beautiful, expensive sound of a kernel panic at 3:44 AM. I\u2019ve been in this rack for three days, and the only thing colder than the air coming out of the perforated floor tiles is the realization that we are building a house of cards on top of a furnace. The log above is the result of trying to push a standard PyTorch 2.2.1 workload across an eight-way H100 SXM5 node using the latest NVIDIA Driver 550.54.14. People talk about &#8220;artificial intelligence&#8221; like it\u2019s some ethereal spirit floating in the cloud. It isn&#8217;t. It\u2019s a series of copper traces screaming under the weight of 700W TDP per socket, and right now, my traces are melting.<\/p>\n<p>The industry is currently obsessed with the H200 and its 141GB of HBM3e memory. They see the 4.8 TB\/s bandwidth and they drool. I see it and I think about the signal integrity issues. I think about the fact that we are trying to pump nearly 5 terabytes of data per second through a package that is smaller than my thumb. The HBM3e &#8220;mirage&#8221; is the idea that more memory bandwidth will solve the fundamental inefficiency of the software stack. It won&#8217;t. You can give a &#8220;software engineer&#8221; a firehose, and they\u2019ll still find a way to use it to fill a thimble one drop at a time because they\u2019re too busy wrapping their Python 3.11.8 code in three layers of Docker containers and a Kubernetes abstraction that nobody actually understands.<\/p>\n<p>We are seeing a 2.4x increase in bandwidth over the A100, but the actual effective throughput in real-world LLM training is nowhere near that. Why? Because the overhead of the NCCL 2.20.5 communication collective is eating the gains alive. You\u2019ve got these massive HBM stacks, but the moment you try to synchronize gradients across a 512-node cluster, the physical reality of light-speed limitations in fiber optics and the latency of the InfiniBand NDR switches starts to bite. You can\u2019t &#8220;code&#8221; your way out of physics. But sure, keep telling me how &#8220;artificial intelligence&#8221; is going to change the world while I\u2019m here replacing a melted QSFP112 cable.<\/p>\n<p>The HBM3e spec is a marvel of engineering, don&#8217;t get me wrong. 141GB at 4.8 TB\/s is insane. But the thermal density is becoming unmanageable. We are reaching the point where air cooling is a physical impossibility. If you aren&#8217;t running direct-to-chip liquid cooling with a secondary loop and a massive CDU (Cooling Distribution Unit), you aren&#8217;t running at peak clock speeds. You\u2019re throttling. You\u2019re paying for 2.2 GHz and getting 1.4 GHz because your &#8220;cold aisle&#8221; is actually 35 degrees Celsius because the CRAC units can&#8217;t keep up with the 40kW racks. It\u2019s a joke. A very expensive, very loud joke.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_05_12_33_%E2%80%93_PDU_Meltdown_and_the_700W_Lie\"><\/span>[LOG_ENTRY_05:12:33] &#8211; PDU Meltdown and the 700W Lie<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I just checked the PDU (Power Distribution Unit) logs for Rack 4. We\u2019re drawing 38.4kW on a rack rated for 40kW. The breakers are humming. That low-frequency buzz is the sound of the &#8220;artificial intelligence&#8221; bubble about to pop, or at least the sound of my Saturday morning disappearing into a cloud of ozone. The industry likes to quote these TDP numbers\u2014700W for an H100, 1200W for a B200. Those are lies. Those are &#8220;thermal design points,&#8221; not peak transient draws. When you hit a massive matrix multiplication kernel in CUDA 12.4, the transient power spikes can blow right past those ratings for milliseconds. Do that across 8 GPUs simultaneously, and your power supply&#8217;s capacitors are doing more work than the actual silicon.<\/p>\n<p>The sheer arrogance of the current hardware cycle is staggering. We are building chips that require their own dedicated substations. I\u2019ve seen &#8220;green&#8221; data centers that claim to be carbon neutral while they\u2019re sucking down enough juice to power a small city just so some &#8220;developer&#8221; can generate a picture of a cat in a tuxedo. It\u2019s a grotesque waste of silicon. We\u2019re taking high-purity sand, refining it with massive amounts of energy, etching it with extreme ultraviolet lithography, and then using it to run Python scripts that spend 40% of their time in garbage collection.<\/p>\n<p>Let\u2019s talk about the PDUs. Most of these legacy data centers were built for 5kW to 10kW racks. Now, the &#8220;artificial intelligence&#8221; crowd wants to drop 100kW Blackwell racks into the same floor space. You can\u2019t just &#8220;upgrade&#8221; that. You need to rip out the entire electrical backbone. You need new transformers, new switchgear, and a prayer. I\u2019m looking at the bus bars in this facility and they\u2019re literally hot to the touch. Not &#8220;warm.&#8221; Hot. And the software guys? They\u2019re complaining that their Jupyter notebook is taking too long to load. They have no concept of the physical cost of a FLOP. To them, a FLOP is a number on a spreadsheet. To me, a FLOP is a unit of heat that I have to move out of this building before the fire suppression system decides to ruin my life.<\/p>\n<p>The 700W TDP is a baseline for a steady state that never exists. In reality, you\u2019re dealing with a dynamic load that swings wildly. If your power delivery network (PDN) on the OAM (Open Accelerator Module) isn&#8217;t perfect, you get voltage droop. Voltage droop leads to bit flips. Bit flips lead to the kernel panic I started this log with. And then I have to spend four hours running Memtest86+ and NVML diagnostics just to prove to some kid with a CS degree that his &#8220;groundbreaking&#8221; model is actually just crashing the hardware because he didn&#8217;t understand memory alignment.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_08_21_55_%E2%80%93_Python_3118_and_the_Bloatware_Stack\"><\/span>[LOG_ENTRY_08:21:55] &#8211; Python 3.11.8 and the Bloatware Stack<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I\u2019m sitting here watching the <code>top<\/code> output on the head node. Python 3.11.8. Why? Why are we still doing this? We are running the most computationally intensive workloads in human history through an interpreted language that was designed to be &#8220;easy to read.&#8221; It\u2019s like trying to win a Formula 1 race while driving a tractor made of LEGOs. <\/p>\n<p>PyTorch 2.2.1 is a massive improvement, sure. <code>torch.compile<\/code> actually tries to do something sensible with the graph. But underneath it all, you\u2019re still dealing with a stack of abstractions so deep that the silicon is a distant memory. You\u2019ve got Python calling C++ wrappers, which call CUDA kernels, which are managed by a driver that is currently shitting the bed because of a race condition in the memory allocator. It\u2019s a miracle anything works at all.<\/p>\n<p>The &#8220;artificial intelligence&#8221; industry has a fundamental disdain for efficiency. When compute was expensive, we wrote tight code. We cared about cache lines. We cared about register pressure. Now? &#8220;Just throw more GPUs at it.&#8221; That\u2019s the mantra. Can\u2019t fit the model? Use DeepSpeed and shard it across 128 nodes. Never mind that your inter-node communication is now 90% of your wall-clock time. Never mind that you\u2019re burning 100,000 kilowatt-hours just to avoid writing a custom CUDA kernel that actually manages memory properly.<\/p>\n<p>I looked at a &#8220;state-of-the-art&#8221; training script yesterday. It had fourteen different library dependencies just for logging. Fourteen. Each one of them importing more junk, bloating the instruction cache, and adding latency. We are using HBM3e with 4.8 TB\/s of bandwidth to move data that is being processed by code that is as efficient as a leaky bucket. The gap between theoretical peak performance and actual achieved MFU (Model Flops Utilization) is widening. We\u2019re lucky if we hit 40% MFU on a good day. The other 60%? Heat. Pure, unadulterated heat. It\u2019s a tax on the power grid paid to the altar of developer laziness.<\/p>\n<p>And don&#8217;t get me started on the documentation. Or the lack thereof. Have you tried to debug a NCCL timeout recently? The error messages are about as helpful as a &#8220;Check Engine&#8221; light in a spaceship. &#8220;Internal Error.&#8221; Great. Thanks. Was it a bit flip in the NVLink fabric? Was it a thermal throttle on GPU 6 that caused a synchronization delay? Or was it just Python being Python and deciding to pause for a garbage collection cycle at the exact moment the collective was supposed to reduce? You\u2019ll never know. You just restart the job and hope the silicon gods are feeling merciful.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_11_05_01_%E2%80%93_Thermal_Throttling_as_a_Business_Model\"><\/span>[LOG_ENTRY_11:05:01] &#8211; Thermal Throttling as a Business Model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The fans in this row are currently spinning at 18,000 RPM. It\u2019s a deafening, high-pitched scream that vibrates in your teeth. If you take your earplugs out for even a second, it feels like someone is driving a needle into your brain. This is the sound of &#8220;artificial intelligence&#8221; in 2024. It\u2019s not a soft voice in a box; it\u2019s a jet engine strapped to a rack.<\/p>\n<p>We are seeing a trend where thermal throttling is no longer an &#8220;emergency&#8221; state\u2014it\u2019s the expected operating condition. The chips are designed to boost until they hit the Tjunction limit (usually around 85\u00b0C to 90\u00b0C for these high-end parts) and then stay there. The problem is that the &#8220;boost&#8221; clock is what the marketing department uses for the FLOPs calculation, but the &#8220;throttled&#8221; clock is what you actually get after twenty minutes of training. <\/p>\n<p>I\u2019ve been benchmarking the H100s under sustained load. After thirty minutes of a heavy Transformer workload, the clock speeds start to jitter. You see these micro-dips in frequency. On a single GPU, it\u2019s not a big deal. But when you have 4,096 GPUs in a cluster, and they\u2019re all throttling at different times because of slight variations in the airflow or the application of the thermal interface material (TIM), your synchronous training job becomes a nightmare. The slowest GPU dictates the speed of the entire cluster. One GPU hits 89\u00b0C and drops its clock by 200MHz, and now 4,095 other GPUs are sitting idle for 50 milliseconds waiting for it to finish its shard. <\/p>\n<p>This is why the &#8220;cloud&#8221; is such a scam for high-end compute. You have no idea what the thermal environment of your &#8220;instance&#8221; is. You\u2019re paying full price for an H100 that might be sitting in a hot spot in some overcrowded data center in Virginia, throttling its brains out while the guy in the next rack is running a crypto miner. You\u2019re paying for silicon you can\u2019t even use. <\/p>\n<p>We need to move to liquid cooling, but the industry is dragging its feet because it\u2019s expensive and &#8220;scary.&#8221; They\u2019d rather keep pumping more air, building bigger fans, and wasting more power. It\u2019s a dead end. We are at the physical limit of what air can do. You can\u2019t move enough molecules of nitrogen and oxygen past a 700W chip to keep it cool without creating a hurricane. But sure, let\u2019s keep pretending that we can just keep scaling these &#8220;artificial intelligence&#8221; models forever. The scaling laws for LLMs don&#8217;t account for the scaling laws of thermodynamics. And thermodynamics always wins.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_14_30_19_%E2%80%93_The_Scaling_Law_Delusion\"><\/span>[LOG_ENTRY_14:30:19] &#8211; The Scaling Law Delusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Everyone is talking about &#8220;scaling laws.&#8221; The idea that if you just add more parameters and more data and more compute, the model gets smarter. It\u2019s a linear fantasy in a non-linear world. We are hitting the point of diminishing returns, not in the math, but in the hardware. The cost to train the next generation of models isn&#8217;t just double the previous one; it\u2019s an order of magnitude more complex because of the infrastructure required.<\/p>\n<p>We\u2019re talking about &#8220;artificial intelligence&#8221; clusters that require 100 megawatts. To put that in perspective, a small nuclear reactor produces about 300 megawatts. We are unironically discussing building nuclear power plants just to train better chatbots. And for what? So we can have a model that\u2019s 5% better at summarizing emails? The valuation models for these companies are based on the idea that compute will continue to get cheaper and more plentiful. But the chip shortages and the power constraints say otherwise.<\/p>\n<p>The HBM3e supply is already spoken for. SK Hynix and Micron are running at 100% capacity and they still can&#8217;t meet the demand. This creates a secondary market where people are paying 3x the MSRP for hardware that will be obsolete in eighteen months. It\u2019s a frenzy. It\u2019s the tulip mania, but with more transistors. And the funniest part is that half the people buying these chips don&#8217;t even have the power or cooling to run them. I\u2019ve seen warehouses full of H100 nodes just sitting there because the local utility company told the owner it would take two years to bring enough power to the building to turn them on.<\/p>\n<p>The &#8220;scaling law&#8221; delusion ignores the physical reality of the supply chain. You can\u2019t just &#8220;scale&#8221; the production of high-purity neon gas or the availability of ASML\u2019s EUV machines. We are tethered to the physical world, no matter how much the &#8220;cloud&#8221; people want to believe otherwise. Every time someone says &#8220;artificial intelligence&#8221; is going to solve the energy crisis, I want to show them the electricity bill for a single training run of a 1.8-trillion parameter model. It\u2019s not a solution; it\u2019s a primary contributor to the problem.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_17_45_44_%E2%80%93_Driver_5505414_and_the_Death_of_Stability\"><\/span>[LOG_ENTRY_17:45:44] &#8211; Driver 550.54.14 and the Death of Stability<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I\u2019ve spent the last six hours trying to figure out why Driver 550.54.14 is causing a segfault in <code>libcuda.so<\/code>. This is the &#8220;latest and greatest&#8221; driver, supposedly optimized for the new CUDA 12.4 features. Instead, it\u2019s a dumpster fire. It seems like NVIDIA is moving so fast to support their new hardware that they\u2019ve completely given up on QA for their software stack. <\/p>\n<p>The kernel panic I logged earlier? It\u2019s a null pointer dereference in the <code>nv_set_system_info<\/code> function. It happens whenever the driver tries to poll the NVLink status while the GPU is under heavy load. The driver is literally tripping over itself. It\u2019s trying to manage a fabric of interconnected GPUs while the GPUs are changing their power states and clock speeds so rapidly that the driver&#8217;s internal state becomes inconsistent. <\/p>\n<p>This is what happens when you have a proprietary, closed-source driver stack. I can\u2019t fix it. I can\u2019t patch it. I just have to wait for NVIDIA to acknowledge the bug, which they won&#8217;t, and then wait for a new version that will probably break three other things. We are all beholden to a single company\u2019s ability to write stable C code, and right now, they are failing.<\/p>\n<p>And the &#8220;artificial intelligence&#8221; researchers don&#8217;t care. They just want their code to run. They don&#8217;t understand that the reason their job failed at 2 AM wasn&#8217;t a &#8220;bug in the model,&#8221; but a fundamental failure of the system software. We\u2019ve built this entire industry on a foundation of shifting sand. We\u2019re using Ubuntu 22.04.4 LTS, which is fine, but the kernel is 5.15 and the driver is this bloated mess, and the whole thing is held together by duct tape and hope.<\/p>\n<p>I miss the days when hardware was predictable. When you knew that if you pushed a chip to a certain frequency, it would stay there. Now, everything is &#8220;opportunistic.&#8221; Opportunistic boosting, opportunistic power management, opportunistic error correction. It\u2019s just a fancy way of saying &#8220;we don&#8217;t know if it will work, but we\u2019ll try.&#8221; It\u2019s not engineering; it\u2019s gambling. And I\u2019m the one who has to stay up all night when the house loses.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"LOG_ENTRY_21_10_02_%E2%80%93_The_Silicon_Graveyard\"><\/span>[LOG_ENTRY_21:10:02] &#8211; The Silicon Graveyard<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I\u2019m looking at a pile of decommissioned A100s in the corner. Three years ago, these were the pinnacle of human achievement. Now, they\u2019re basically e-waste. The pace of this &#8220;artificial intelligence&#8221; arms race is creating a silicon graveyard of staggering proportions. We are burning through hardware at a rate that is completely unsustainable. <\/p>\n<p>The B200 is coming, and it\u2019s going to make the H100 look like a calculator. 208 billion transistors. 192GB of HBM3e. 8 TB\/s of bandwidth. And a power draw that will probably require its own dedicated cooling tower. We are chasing a dragon that we can never catch. Every time we get more compute, we just find more ways to waste it. We create bigger models with more parameters that don&#8217;t actually show any more &#8220;intelligence,&#8221; they just have more memorized data.<\/p>\n<p>The physical reality of the data center is that we are running out of space, running out of power, and running out of patience. My hands are covered in thermal paste and my eyes are bloodshot from staring at terminal logs. The &#8220;artificial intelligence&#8221; revolution isn&#8217;t happening in some clean, white room with glowing blue lights. It\u2019s happening here, in the dark, in the cold aisle, amidst the smell of hot electronics and the roar of fans. <\/p>\n<p>I\u2019m done. The driver is still crashing, the PDU is still humming, and I\u2019ve got another 48 hours of stress tests to run. The silicon doesn&#8217;t care about your dreams of AGI. The silicon only cares about voltage and temperature. And right now, the temperature is rising. I can feel the heat through my boots. The floor tiles are vibrating. Somewhere, a capacitor is about to give up the ghost, and I\u2019ll be the one who has to find it. This isn&#8217;t the future I was promised. This is just a very loud, very expensive way to turn electricity into noise.<\/p>\n<p>I&#8217;m out of coffee. I&#8217;m out of sleep. And I&#8217;m definitely out of patience for anyone who uses the word &#8220;cloud&#8221; without knowing what a torque wrench is for. The next person who asks me about &#8220;artificial intelligence&#8221; is getting a 400-gram copper heatsink thrown at their head. Let&#8217;s see how their &#8220;neural network&#8221; handles that physical input.<\/p>\n<p>Log ends. System still unstable. Throttling at 87\u00b0C. Send more fans. Or a fire extinguisher. Actually, just cut the power. Let the silicon rest. It\u2019s earned it. We haven&#8217;t.<\/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\/install-php-in-ubuntu-18-04\/\">Install Php In Ubuntu 18 04<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/html-guide\/\">Html Guide<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/understanding-machine-learning-models-a-complete-guide-2\/\">Understanding Machine Learning Models A Complete Guide 2<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>text [ 2592.148203] NVRM: GPU at PCI:0000:01:00: GPU-8d7f4a2b-3c1e-4f5d-9a8b-7c6d5e4f3a2b [ 2592.148210] NVRM: Xid (PCI:0000:01:00): 31, GPU memory page fault in isolation [ 2592.148215] nvidia-nvlink: Unregistered the Nvlink Core, major device number 234 [ 2592.148222] NVRM: os_schedule: Attempted to yield the CPU while holding a spinlock! [ 2592.148230] BUG: kernel NULL pointer dereference, address: 0000000000000000 [ 2592.148235] &#8230; <a title=\"artificial intelligence news &#8211; Guide\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/artificial-intelligence-news-guide\/\" aria-label=\"Read more  on artificial intelligence news &#8211; Guide\">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-4758","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>artificial intelligence news - Guide - 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\/artificial-intelligence-news-guide\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"artificial intelligence news - Guide - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"text [ 2592.148203] NVRM: GPU at PCI:0000:01:00: GPU-8d7f4a2b-3c1e-4f5d-9a8b-7c6d5e4f3a2b [ 2592.148210] NVRM: Xid (PCI:0000:01:00): 31, GPU memory page fault in isolation [ 2592.148215] nvidia-nvlink: Unregistered the Nvlink Core, major device number 234 [ 2592.148222] NVRM: os_schedule: Attempted to yield the CPU while holding a spinlock! [ 2592.148230] BUG: kernel NULL pointer dereference, address: 0000000000000000 [ 2592.148235] ... 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[ 2592.148230] BUG: kernel NULL pointer dereference, address: 0000000000000000 [ 2592.148235] ... 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