{"id":17026,"date":"2026-06-01T09:00:54","date_gmt":"2026-06-01T08:00:54","guid":{"rendered":"https:\/\/ibertronica.es\/blog\/?p=17026"},"modified":"2026-05-29T09:53:08","modified_gmt":"2026-05-29T08:53:08","slug":"nvidia-h100-vs-h200-which-gpu-to-choose-for-training-ai-models-in-2026","status":"publish","type":"post","link":"https:\/\/ibertronica.es\/blog\/en\/news-en\/nvidia-h100-vs-h200-which-gpu-to-choose-for-training-ai-models-in-2026\/","title":{"rendered":"NVIDIA H100 vs H200: Which GPU to choose for training AI models in 2026"},"content":{"rendered":"<p>The race for <strong>artificial intelligence<\/strong> has turned high-performance GPUs into one of the most sought-after technological resources in the world.<\/p>\n<p>And within the NVIDIA ecosystem, two names currently dominate the enterprise AI market:<\/p>\n<ul>\n<li><strong>NVIDIA H100<\/strong><\/li>\n<li><strong>NVIDIA H200<\/strong><\/li>\n<\/ul>\n<p>Both GPUs are designed for <strong>training and inference of large-scale artificial intelligence models<\/strong>, especially LLMs, multimodal systems and HPC workloads.<\/p>\n<p>However, although they share the Hopper architecture and many technical elements, there are important differences that can have a huge impact depending on the type of project.<\/p>\n<p>In this comparison, we analyse:<\/p>\n<ul>\n<li>What really changes between H100 and H200.<\/li>\n<li>When it is worth paying more for H200.<\/li>\n<li>What performance they offer in modern AI.<\/li>\n<li>How VRAM and bandwidth affect performance.<\/li>\n<li>Which option is more interesting depending on budget and use case.<\/li>\n<\/ul>\n<p>In addition, we will discuss real availability in Spain and alternatives such as the <strong>RTX PRO 6000 Blackwell<\/strong> for projects with tighter budgets.<\/p>\n<h2>Hopper architecture: what H100 and H200 have in common<\/h2>\n<p>Both the <strong>NVIDIA H100<\/strong> and the <strong>NVIDIA H200<\/strong> are based on the Hopper architecture.<\/p>\n<p>This means that both share much of their technological DNA:<\/p>\n<ul>\n<li>4th generation Tensor Cores.<\/li>\n<li>FP8 Transformer Engine.<\/li>\n<li>NVLink support.<\/li>\n<li>CUDA compatibility.<\/li>\n<li>Support for distributed training.<\/li>\n<li>Optimisation for LLMs.<\/li>\n<li>Compatibility with HGX and DGX.<\/li>\n<\/ul>\n<p>In architectural terms, this is not a full generational leap.<\/p>\n<p>The H200 does not replace Hopper with a new architecture; instead, it represents an evolution mainly focused on <strong>memory and bandwidth<\/strong>.<\/p>\n<p>And that is precisely the key.<\/p>\n<h2>The key difference: HBM3 vs HBM3e<\/h2>\n<p>The most important improvement of the <strong>NVIDIA H200<\/strong> over the <strong>H100<\/strong> is not in the CUDA cores or the architecture.<\/p>\n<p>It is in the memory.<\/p>\n<h3>NVIDIA H100<\/h3>\n<p>The H100 uses:<\/p>\n<ul>\n<li><strong>80 GB HBM3<\/strong>.<\/li>\n<li>Up to <strong>3.35 TB\/s of bandwidth<\/strong>.<\/li>\n<\/ul>\n<h3>NVIDIA H200<\/h3>\n<p>The H200 features:<\/p>\n<ul>\n<li><strong>141 GB HBM3e<\/strong>.<\/li>\n<li>Up to <strong>4.8 TB\/s of bandwidth<\/strong>.<\/li>\n<\/ul>\n<p>The difference is huge.<\/p>\n<p>Especially in modern AI workloads, where memory has become one of the biggest bottlenecks.<\/p>\n<p><a href=\"https:\/\/ibertronica.es\/componentes\/integracion\/tarjetas-graficas-pro\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-17023 size-full\" title=\"Nvidia H100 Vs H200 2\" src=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-2.jpg\" alt=\"Nvidia H100 Vs H200 2\" width=\"1000\" height=\"571\" srcset=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-2.jpg 1000w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-2-300x171.jpg 300w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-2-150x86.jpg 150w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-2-203x116.jpg 203w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/a><\/p>\n<h2>Why is memory so important in AI?<\/h2>\n<p>Today\u2019s models consume massive amounts of memory.<\/p>\n<p>Especially:<\/p>\n<ul>\n<li>LLMs.<\/li>\n<li>RAG.<\/li>\n<li>Fine-tuning.<\/li>\n<li>Mixture of Experts.<\/li>\n<li>Huge context windows.<\/li>\n<li>Batch inference.<\/li>\n<\/ul>\n<p>The problem is no longer just raw computing power.<\/p>\n<p>The real challenge is often:<\/p>\n<ul>\n<li>Fitting the entire model into VRAM.<\/li>\n<li>Constantly feeding the Tensor Cores.<\/li>\n<li>Avoiding slow transfers from RAM or storage.<\/li>\n<\/ul>\n<p>This is where the <strong>H200<\/strong> makes a very significant difference.<\/p>\n<p>With <strong>141 GB of HBM3e memory<\/strong>:<\/p>\n<ul>\n<li>More models can fit entirely on the GPU.<\/li>\n<li>The need for tensor parallelism is reduced.<\/li>\n<li>Energy efficiency improves.<\/li>\n<li>Throughput increases.<\/li>\n<li>Bottlenecks are reduced.<\/li>\n<\/ul>\n<p>In large models, this can translate into very significant improvements.<\/p>\n<h2>Complete comparison table: NVIDIA H100 vs H200<\/h2>\n<table style=\"width: 100%;\" border=\"0\" cellspacing=\"1\" cellpadding=\"1\">\n<tbody>\n<tr>\n<td><strong>Feature<\/strong><\/td>\n<td><strong>NVIDIA H100<\/strong><\/td>\n<td><strong>NVIDIA H200<\/strong><\/td>\n<\/tr>\n<tr>\n<td><strong>Architecture<\/strong><\/td>\n<td>Hopper<\/td>\n<td>Hopper<\/td>\n<\/tr>\n<tr>\n<td><strong>Memory<\/strong><\/td>\n<td>80 GB HBM3<\/td>\n<td>141 GB HBM3e<\/td>\n<\/tr>\n<tr>\n<td><strong>Memory bandwidth<\/strong><\/td>\n<td>3.35 TB\/s<\/td>\n<td>4.8 TB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>Tensor Cores<\/strong><\/td>\n<td>4th gen<\/td>\n<td>4th gen<\/td>\n<\/tr>\n<tr>\n<td><strong>FP8 Transformer Engine<\/strong><\/td>\n<td>Yes<\/td>\n<td>Yes<\/td>\n<\/tr>\n<tr>\n<td><strong>NVLink<\/strong><\/td>\n<td>Yes<\/td>\n<td>Yes<\/td>\n<\/tr>\n<tr>\n<td><strong>Approx. TDP<\/strong><\/td>\n<td>700W<\/td>\n<td>700W<\/td>\n<\/tr>\n<tr>\n<td><strong>PCIe \/ SXM<\/strong><\/td>\n<td>Both<\/td>\n<td>Both<\/td>\n<\/tr>\n<tr>\n<td><strong>Target use<\/strong><\/td>\n<td>AI\/HPC<\/td>\n<td>Advanced AI\/HPC<\/td>\n<\/tr>\n<tr>\n<td><strong>AI performance<\/strong><\/td>\n<td>Very high<\/td>\n<td>Superior in memory-bound models<\/td>\n<\/tr>\n<tr>\n<td><strong>Estimated price<\/strong><\/td>\n<td>Lower<\/td>\n<td>Higher<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The table makes one thing clear:<\/p>\n<p>The <strong>H200<\/strong> is not designed to revolutionise Hopper.<\/p>\n<p>It is designed to solve one of the biggest problems in modern AI: <strong>memory<\/strong>.<\/p>\n<h2>Performance in LLM model training<\/h2>\n<p>Training is where the difference between both GPUs is most noticeable.<\/p>\n<p>Especially in:<\/p>\n<ul>\n<li>70B models.<\/li>\n<li>405B models.<\/li>\n<li>Long context windows.<\/li>\n<li>Complex fine-tuning.<\/li>\n<li>Distributed training.<\/li>\n<\/ul>\n<h3>When the model is memory-bound<\/h3>\n<p>Many current workloads are no longer limited by pure compute.<\/p>\n<p>They are limited by:<\/p>\n<ul>\n<li>Available memory.<\/li>\n<li>Bandwidth.<\/li>\n<li>Transfers between GPUs.<\/li>\n<\/ul>\n<p>In these cases, the <strong>H200<\/strong> usually offers major improvements over the H100.<\/p>\n<p>Especially in:<\/p>\n<ul>\n<li>Tokens processed per second.<\/li>\n<li>Sustained throughput.<\/li>\n<li>Multi-GPU scalability.<\/li>\n<li>Overall energy efficiency.<\/li>\n<\/ul>\n<h3>Llama 70B and similar models<\/h3>\n<p>In models such as <strong>Llama 70B<\/strong>:<\/p>\n<ul>\n<li>H100 still offers excellent performance.<\/li>\n<li>H200 allows larger batch sizes.<\/li>\n<li>Offloading operations are reduced.<\/li>\n<li>GPU utilisation improves.<\/li>\n<\/ul>\n<p>In very intensive workloads, the difference can be clearly noticeable.<\/p>\n<h3>Giant models and MoE<\/h3>\n<p>In extremely large models:<\/p>\n<ul>\n<li>Mixture of Experts.<\/li>\n<li>405B.<\/li>\n<li>Multimodal models.<\/li>\n<li>Complex distributed training.<\/li>\n<\/ul>\n<p>The <strong>H200<\/strong> starts to clearly justify its additional cost.<\/p>\n<p>Because the extra bandwidth and additional memory reduce major bottlenecks.<\/p>\n<h2>Inference performance: tokens per second and efficiency<\/h2>\n<p>Inference is another interesting scenario.<\/p>\n<p>Here, the most powerful GPU does not always win automatically.<\/p>\n<p>It depends heavily on:<\/p>\n<ul>\n<li>Model size.<\/li>\n<li>Quantisation level.<\/li>\n<li>Batch size.<\/li>\n<li>Required latency.<\/li>\n<li>Number of concurrent users.<\/li>\n<\/ul>\n<h3>Inference of small and medium-sized models<\/h3>\n<p>For models:<\/p>\n<ul>\n<li>7B.<\/li>\n<li>13B.<\/li>\n<li>34B.<\/li>\n<\/ul>\n<p>The <strong>H100<\/strong> remains an extremely solid solution.<\/p>\n<p>In many cases, even an <strong>RTX PRO 6000 Blackwell<\/strong> can offer a more attractive performance\/price ratio.<\/p>\n<h3>Large-scale enterprise inference<\/h3>\n<p>The <strong>H200<\/strong> starts to stand out when we talk about:<\/p>\n<ul>\n<li>Large inference batches.<\/li>\n<li>Long context windows.<\/li>\n<li>Multi-user systems.<\/li>\n<li>Complex AI agents.<\/li>\n<li>Multimodal inference.<\/li>\n<\/ul>\n<p>The additional bandwidth clearly improves throughput.<\/p>\n<p>And that is especially important in enterprise environments where thousands or millions of tokens are served continuously.<\/p>\n<h2>H100: when it is still enough<\/h2>\n<p>Despite the huge media attention around the <strong>H200<\/strong>, the <strong>H100<\/strong> remains an outstanding GPU.<\/p>\n<p>In fact, for many companies it continues to be the most balanced option.<\/p>\n<h3>When we recommend H100<\/h3>\n<p>The <strong>H100<\/strong> still makes a lot of sense when:<\/p>\n<ul>\n<li>Budget is important.<\/li>\n<li>You work with medium-sized models.<\/li>\n<li>You fine-tune 7B\u201370B models.<\/li>\n<li>The cluster is already designed around Hopper.<\/li>\n<li>Availability is a priority.<\/li>\n<li>Cost per GPU matters more than maximum performance.<\/li>\n<\/ul>\n<h3>Excellent mature ecosystem<\/h3>\n<p>In addition, the <strong>H100<\/strong> has:<\/p>\n<ul>\n<li>A highly consolidated ecosystem.<\/li>\n<li>Very wide adoption.<\/li>\n<li>Many public benchmarks.<\/li>\n<li>Validated infrastructure.<\/li>\n<li>Great availability in the cloud.<\/li>\n<\/ul>\n<p>In practice, it remains the de facto standard for many AI projects.<\/p>\n<h2>H200: when it makes a real difference<\/h2>\n<p>The <strong>H200<\/strong> starts to clearly justify its cost when memory becomes the main bottleneck.<\/p>\n<h3>Use cases where H200 stands out<\/h3>\n<p>We especially recommend <strong>H200<\/strong> for:<\/p>\n<ul>\n<li>Training giant LLMs.<\/li>\n<li>Intensive enterprise inference.<\/li>\n<li>Large context windows.<\/li>\n<li>Complex RAG.<\/li>\n<li>MoE.<\/li>\n<li>Scientific HPC.<\/li>\n<li>Multimodal systems.<\/li>\n<\/ul>\n<h3>Fewer GPUs required<\/h3>\n<p>Another important aspect:<\/p>\n<p>In some scenarios, the <strong>H200<\/strong> can reduce the total number of GPUs required.<\/p>\n<p>And this can partially offset the higher unit cost.<\/p>\n<p>Because reducing GPUs also means:<\/p>\n<ul>\n<li>Fewer nodes.<\/li>\n<li>Lower power consumption.<\/li>\n<li>Fewer switches.<\/li>\n<li>Less NVLink complexity.<\/li>\n<li>Lower operating cost.<\/li>\n<\/ul>\n<p>In large clusters, this can have a huge impact.<\/p>\n<h2>Power consumption, cooling and density<\/h2>\n<p>Both <strong>H100<\/strong> and <strong>H200<\/strong> are extremely demanding GPUs.<\/p>\n<p>Especially in SXM format.<\/p>\n<h3>High TDP<\/h3>\n<p>Both solutions are around:<\/p>\n<ul>\n<li><strong>700W<\/strong>.<\/li>\n<\/ul>\n<p>This requires:<\/p>\n<ul>\n<li>Specialised servers.<\/li>\n<li>Optimised cooling.<\/li>\n<li>Enterprise power supplies.<\/li>\n<li>Suitable electrical infrastructure.<\/li>\n<\/ul>\n<h3>Liquid cooling<\/h3>\n<p>More and more advanced AI clusters are using:<\/p>\n<ul>\n<li>Liquid cooling.<\/li>\n<li>Direct-to-chip cooling.<\/li>\n<li>Immersion cooling.<\/li>\n<\/ul>\n<p>Especially in HGX deployments with multiple GPUs.<\/p>\n<p><a href=\"https:\/\/ibertronica.es\/componentes\/integracion\/tarjetas-graficas-pro\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-17024 size-full\" title=\"Nvidia H100 Vs H200 3\" src=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-3.jpg\" alt=\"Nvidia H100 Vs H200 3\" width=\"1000\" height=\"571\" srcset=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-3.jpg 1000w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-3-300x171.jpg 300w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-3-150x86.jpg 150w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/NVIDIA-H100-vs-H200-3-203x116.jpg 203w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/a><\/p>\n<h2>NVIDIA HGX: the real environment for these GPUs<\/h2>\n<p>Although PCIe versions exist, the natural environment for both <strong>H100<\/strong> and <strong>H200<\/strong> is usually:<\/p>\n<ul>\n<li>NVIDIA HGX.<\/li>\n<li>NVIDIA DGX.<\/li>\n<li>Enterprise GPU servers.<\/li>\n<\/ul>\n<h3>Why?<\/h3>\n<p>Because these platforms enable:<\/p>\n<ul>\n<li>Ultra-high-speed NVLink.<\/li>\n<li>Efficient multi-GPU scaling.<\/li>\n<li>Optimised topologies.<\/li>\n<li>Adequate power delivery.<\/li>\n<li>Enterprise-grade cooling.<\/li>\n<\/ul>\n<p>In modern AI, performance no longer depends solely on a single isolated GPU.<\/p>\n<p>Interconnection between GPUs is critical.<\/p>\n<h2>Availability and lead times in Spain<\/h2>\n<p>One of the most important factors today is availability.<\/p>\n<p>And here, the situation changes constantly.<\/p>\n<h3>H100<\/h3>\n<p>The <strong>H100<\/strong> already has:<\/p>\n<ul>\n<li>A more mature supply chain.<\/li>\n<li>Greater availability.<\/li>\n<li>More integration options.<\/li>\n<li>More enterprise stock.<\/li>\n<\/ul>\n<h3>H200<\/h3>\n<p>The <strong>H200<\/strong> still has:<\/p>\n<ul>\n<li>Huge demand.<\/li>\n<li>More limited availability.<\/li>\n<li>Longer lead times.<\/li>\n<li>Priority for major integrators and hyperscalers.<\/li>\n<\/ul>\n<p>That is why many companies continue to choose the <strong>H100<\/strong> to avoid deployment delays.<\/p>\n<h2>Alternatives: RTX PRO 6000 Blackwell and Blackwell B200<\/h2>\n<p>Not every project needs an <strong>H100<\/strong> or an <strong>H200<\/strong>.<\/p>\n<p>And here it is important to be honest.<\/p>\n<h3>RTX PRO 6000 Blackwell<\/h3>\n<p>For:<\/p>\n<ul>\n<li>Fine-tuning.<\/li>\n<li>Local inference.<\/li>\n<li>AI workstations.<\/li>\n<li>Development.<\/li>\n<li>Small teams.<\/li>\n<\/ul>\n<p>The <strong>RTX PRO 6000 Blackwell<\/strong> can be a much more cost-effective alternative.<\/p>\n<p>Especially when:<\/p>\n<ul>\n<li>Huge clusters are not required.<\/li>\n<li>Budget is limited.<\/li>\n<li>You work with quantised models.<\/li>\n<li>Workstation flexibility is a priority.<\/li>\n<\/ul>\n<p><strong>Are you looking for a professional GPU for AI, rendering or advanced computing?<\/strong><br \/>\nExplore our selection of professional graphics cards and find the most suitable solution for your project.<\/p>\n<p><a href=\"https:\/\/ibertronica.es\/componentes\/integracion\/tarjetas-graficas-pro\"><strong>View professional graphics cards<\/strong><\/a><\/p>\n<h3>Blackwell B200<\/h3>\n<p>At the other end of the spectrum is the <strong>B200<\/strong>.<\/p>\n<p>Here, we are talking about a much deeper generational leap.<\/p>\n<p>Blackwell promises:<\/p>\n<ul>\n<li>Much higher performance.<\/li>\n<li>Better efficiency.<\/li>\n<li>More capacity for giant models.<\/li>\n<li>Better scalability.<\/li>\n<\/ul>\n<p>However, costs and availability remain important factors.<\/p>\n<h2>Which GPU do we actually recommend?<\/h2>\n<p>The answer depends entirely on the project.<\/p>\n<h3>We recommend H100 when:<\/h3>\n<ul>\n<li>You are looking for a balance between cost and performance.<\/li>\n<li>You work with medium-sized models.<\/li>\n<li>You need fast availability.<\/li>\n<li>Budget matters.<\/li>\n<li>The Hopper cluster already exists.<\/li>\n<\/ul>\n<h3>H200 is recommended when:<\/h3>\n<ul>\n<li>Memory is the bottleneck.<\/li>\n<li>You work with huge models.<\/li>\n<li>You need maximum throughput.<\/li>\n<li>You want to minimise offloading.<\/li>\n<li>The goal is to scale to a large size.<\/li>\n<\/ul>\n<h3>We recommend RTX PRO 6000 Blackwell when:<\/h3>\n<ul>\n<li>You need a workstation.<\/li>\n<li>You run local inference.<\/li>\n<li>You work in AI development.<\/li>\n<li>Your budget is much tighter.<\/li>\n<\/ul>\n<h2>Frequently asked questions<\/h2>\n<h3>Is the H200 much faster than the H100?<\/h3>\n<p>It depends on the workload.<\/p>\n<p>In workloads limited by memory and bandwidth, the difference can be very significant.<\/p>\n<p>In more compute-bound workloads, the difference is smaller.<\/p>\n<h3>Is it worth waiting for Blackwell?<\/h3>\n<p>It depends on the project and the deadlines.<\/p>\n<p>For many current deployments, Hopper remains fully valid.<\/p>\n<h3>Which is better for inference?<\/h3>\n<p>It depends on the model size and the required throughput.<\/p>\n<p>For large-scale enterprise inference, <strong>H200<\/strong> has clear advantages.<\/p>\n<h3>Is the H100 still recommended in 2026?<\/h3>\n<p>Yes.<\/p>\n<p>It remains one of the most powerful and widely used GPUs in the AI market.<\/p>\n<h3>Can these GPUs be used in a workstation?<\/h3>\n<p>In some cases yes, especially PCIe versions.<\/p>\n<p>But these GPUs are generally designed for specialised servers.<\/p>\n<h2>Conclusion<\/h2>\n<p>The <strong>NVIDIA H100<\/strong> and <strong>H200<\/strong> currently represent two of the most powerful platforms in the world for artificial intelligence.<\/p>\n<p>The main difference is not so much the architecture as the memory.<\/p>\n<p>And in modern AI, memory matters a lot.<\/p>\n<p>The <strong>H100<\/strong> remains an extraordinary option for most companies.<\/p>\n<p>But the <strong>H200<\/strong> starts to make a very clear difference in:<\/p>\n<ul>\n<li>Giant LLMs.<\/li>\n<li>Massive inference.<\/li>\n<li>Large context windows.<\/li>\n<li>Memory-bound workloads.<\/li>\n<\/ul>\n<p>Choosing correctly depends on:<\/p>\n<ul>\n<li>Type of models.<\/li>\n<li>Scalability.<\/li>\n<li>Budget.<\/li>\n<li>Availability.<\/li>\n<li>Project objectives.<\/li>\n<\/ul>\n<p>At Ibertr\u00f3nica, we help companies and technology centres design GPU infrastructure adapted to real AI workloads.<\/p>\n<p>From advanced workstations to multi-GPU HGX clusters.<\/p>\n<h2>Configure your GPU server for AI<\/h2>\n<p>If you are considering deploying infrastructure with <strong>NVIDIA H100<\/strong> or <strong>H200<\/strong>, our team can help you define the best configuration according to:<\/p>\n<ul>\n<li>Number of GPUs.<\/li>\n<li>Type of training.<\/li>\n<li>Inference.<\/li>\n<li>Scalability.<\/li>\n<li>Budget.<\/li>\n<li>Cooling.<\/li>\n<li>Data centre integration.<\/li>\n<\/ul>\n<p><strong>Not sure which GPU your artificial intelligence project needs?<\/strong><br \/>\nOur team can help you define the most suitable configuration based on model type, number of GPUs, cooling, budget and scalability.<\/p>\n<p><a onclick=\"javascript:pageTracker._trackPageview('\/mailto\/comercial@ibertronica.es');\"  href=\"mailto:comercial@ibertronica.es\"><strong>Request personalised advice<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The race for artificial intelligence has turned high-performance GPUs into one of the most sought-after technological resources in the world. And within the NVIDIA ecosystem, two names currently dominate the enterprise AI market: NVIDIA H100 NVIDIA H200 Both GPUs are designed for training and inference of large-scale artificial intelligence models, especially LLMs, multimodal systems and HPC workloads. However, although they share the Hopper architecture and many technical elements, there are important differences that can have a huge impact depending on&hellip;<\/p>\n","protected":false},"author":2,"featured_media":17022,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1410],"tags":[5007,5009,5011],"class_list":["post-17026","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en","tag-gpu-en","tag-h200-en","tag-nvidia-h100-en","post-has-thumbnail"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>NVIDIA H100 vs H200: A comparison for AI and LLMs in 2026 - Blog de tecnolog\u00eda<\/title>\n<meta name=\"description\" content=\"NVIDIA H100 vs H200 comparison for artificial intelligence, LLM training, and advanced inference. 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