{"id":1746,"date":"2026-11-05T12:32:32","date_gmt":"2026-11-05T12:32:32","guid":{"rendered":"https:\/\/www.senetic.co.uk\/blog\/?p=1746"},"modified":"2026-11-06T08:46:28","modified_gmt":"2026-11-06T08:46:28","slug":"blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai","status":"publish","type":"post","link":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/","title":{"rendered":"Blackwell, Ada or Ampere? Choosing a GPU generation for enterprise AI"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The GPU generation determines which numerical formats (precisions) the processor handles AI workloads fastest, its memory bandwidth and the card&#8217;s power requirements. Three generations coexist in the NVIDIA professional GPU portfolio: <strong>Ampere (e.g. RTX A6000), Ada Lovelace (e.g. RTX 6000 Ada) and Blackwell (e.g. RTX PRO 6000 Blackwell).<\/strong> Each successive generation supports lower compute precisions \u2013 from BF16 in Ampere to FP4 in Blackwell.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The choice of generation depends on model size, compute format, number of users and hardware operating conditions. GPU types, VRAM capacity, and OEM, Retail and Bulk variants are <a href=\"https:\/\/www.senetic.co.uk\/category\/ai-gpus-30829\/\" target=\"_blank\" rel=\"noreferrer noopener\">covered in the GPUs for AI<\/a>. This article focuses on the differences between generations and editions. It is intended for IT managers, infrastructure architects, data science teams and hardware procurement professionals selecting GPUs for enterprise AI projects.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_87_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><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><\/span><\/a><\/span><\/div>\n<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=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#How_to_identify_the_generation_from_an_NVIDIA_GPU_name\" >How to identify the generation from an NVIDIA GPU name<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#What_changes_between_generations_for_AI_workloads\" >What changes between generations for AI workloads<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#When_to_choose_a_Blackwell-generation_GPU\" >When to choose a Blackwell-generation GPU<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#When_an_Ampere_or_Ada_Lovelace_GPU_meets_the_requirements\" >When an Ampere or Ada Lovelace GPU meets the requirements<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#Same_generation_different_editions_%E2%80%93_using_the_RTX_PRO_6000_Blackwell_as_an_example\" >Same generation, different editions \u2013 using the RTX PRO 6000 Blackwell as an example<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#GPU_generation_and_edition_by_AI_workload\" >GPU generation and edition by AI workload<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"#\" data-href=\"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#Frequently_asked_questions_about_GPU_generations_for_AI\" >Frequently asked questions about GPU generations for AI<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_identify_the_generation_from_an_NVIDIA_GPU_name\"><\/span><strong>How to identify the generation from an NVIDIA GPU name<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The generation of an NVIDIA GPU can be identified by the designation in its model name. <strong>The number in the name (e.g. 2000, 4000, 6000) indicates the card&#8217;s performance tier within a given generation: the higher the number, the more cores and memory it has<\/strong>. This is why the same tier appears under different names in successive generations, for example: RTX A6000 (Ampere), RTX 6000 Ada (Ada Lovelace) and RTX PRO 6000 Blackwell (Blackwell).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Generation<\/strong><\/th><th><strong>Year<\/strong><\/th><th><strong>Workstation GPUs<\/strong><\/th><th><strong>Server GPUs<\/strong><\/th><th><strong>Examples in the portfolio<\/strong><\/th><\/tr><\/thead><tbody><tr><td>Blackwell<\/td><td>2025<\/td><td>RTX PRO + number + Blackwell<\/td><td>RTX PRO + number + Blackwell Server Edition<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTXPRO2000-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX PRO 2000 Blackwell<\/a><\/td><\/tr><tr><td>Ada Lovelace<\/td><td>2022<\/td><td>RTX + number + Ada<\/td><td>L + number<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/900-2G193-0000-001\" target=\"_blank\" rel=\"noopener noreferrer\">L4<\/a><\/td><\/tr><tr><td>Ampere<\/td><td>2020<\/td><td>RTX A + number<\/td><td>A + number<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTXA1000-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX A1000<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_changes_between_generations_for_AI_workloads\"><\/span><strong>What changes between generations for AI workloads<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Successive generations differ primarily in <strong>supported compute precisions<\/strong>, <strong>memory bandwidth and power consumption<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The growing importance of lower precisions is confirmed by the <a href=\"https:\/\/hai.stanford.edu\/ai-index\/2026-ai-index-report\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">AI Index 2026<\/a> report from Stanford University: reduced-precision computing has become standard in many applications, both for model training and inference. According to the report&#8217;s authors, today&#8217;s state-of-the-art machine learning hardware delivers approximately 10 times more computations per watt than it did a decade ago. Each GPU generation pushes this boundary further: Ampere accelerates BF16 computations, Ada Lovelace added FP8, and Blackwell adds FP4 and FP6.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The comparison below covers three GPUs from the same tier (6000).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Specification<\/strong><\/th><th><strong>RTX A6000 (Ampere)<\/strong><\/th><th><strong>RTX 6000 Ada (Ada Lovelace)<\/strong><\/th><th><strong>RTX PRO 6000 Blackwell Workstation Edition<\/strong><\/th><\/tr><\/thead><tbody><tr><td>Tensor Core generation<\/td><td>3.<\/td><td>4.<\/td><td>5.<\/td><\/tr><tr><td>Precisions accelerated by Tensor Cores<\/td><td>TF32, BF16, FP16, INT8<\/td><td>same as Ampere + FP8<\/td><td>same as Ada + FP6, FP4<\/td><\/tr><tr><td>Memory<\/td><td>48 GB GDDR6<\/td><td>48 GB GDDR6<\/td><td>96 GB GDDR7<\/td><\/tr><tr><td>Memory bandwidth<\/td><td>768 GB\/s<\/td><td>960 GB\/s<\/td><td>1792 GB\/s<\/td><\/tr><tr><td>FP16 Tensor performance<\/td><td>154,8 TFLOPS<\/td><td>364 TFLOPS<\/td><td>503,8 TFLOPS<\/td><\/tr><tr><td>Power consumption (TGP)<\/td><td>300 W<\/td><td>300 W<\/td><td>600 W<\/td><\/tr><tr><td>Interface<\/td><td>PCIe 4.0<\/td><td>PCIe 4.0<\/td><td>PCIe 5.0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Ampere<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Ampere (2020) introduced third-generation Tensor Cores and the TF32 and BF16 formats, widely used for model training. GPUs from this generation do not provide hardware acceleration for FP8 computations. Higher-end workstation GPUs in the RTX A series, such as RTX A6000, support NVLink bridges for connecting two cards. The compact RTX A1000 and RTX A400 are well suited to low-power workstations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Ada Lovelace<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Ada Lovelace (2022) introduced fourth-generation Tensor Cores with support for FP8, which reduces memory requirements and, according to the manufacturer, delivers up to 4 times higher inference performance than the previous generation. This generation includes RTX Ada workstation GPUs and the L4, L40 and L40S server accelerators.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Blackwell<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Blackwell (the RTX PRO series from 2025) introduces fifth-generation Tensor Cores supporting FP4 and FP6, as well as GDDR7 memory. According to NVIDIA, a model in FP4 uses less than half the memory required in FP16. According to manufacturer data, the RTX PRO 6000 Blackwell generates an image using the FLUX.dev model in approximately 7 s in BF16 and 3,3 s in FP4.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"When_to_choose_a_Blackwell-generation_GPU\"><\/span><strong>When to choose a Blackwell-generation GPU<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The Blackwell generation is well suited to organisations that want to run large models on a single GPU, use FP4 quantisation or share one card across multiple teams. <\/strong>These capabilities result from specific architectural features:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Support for FP4 and FP6<\/strong> \u2013 lower precision enables a larger model to run within the same memory capacity and accelerates inference. Fifth-generation Tensor Cores process FP4 with twice the throughput of FP8.<\/li>\n\n\n\n<li><strong>GDDR7 memory<\/strong> \u2013 the newer memory standard provides higher bandwidth than GDDR6. The top models in the generation have twice the memory capacity and twice the bandwidth of their previous-generation counterparts.<\/li>\n\n\n\n<li><strong>MIG (Multi-Instance GPU)<\/strong> \u2013 a single card can be partitioned into several isolated instances. Each has its own memory and compute cores, allowing multiple workloads or teams to work in parallel without affecting one another&#8217;s performance.<\/li>\n\n\n\n<li><strong>PCIe 5.0<\/strong> \u2013 twice the bus bandwidth of PCIe 4.0 speeds up data exchange with the processor and system memory.<\/li>\n\n\n\n<li><strong>Wide choice of tiers<\/strong> \u2013 the RTX PRO series includes both compact cards for small form factors and high-memory-capacity models for the most demanding workloads.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">When planning a configuration, it is worth considering power consumption: the RTX PRO 6000 Blackwell Workstation Edition requires up to 600 W, twice as much as the RTX 6000 Ada. This affects the choice of power supply and cooling and, when using multiple cards, the choice of edition discussed later in this article.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"When_an_Ampere_or_Ada_Lovelace_GPU_meets_the_requirements\"><\/span><strong>When an Ampere or Ada Lovelace GPU meets the requirements<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Ampere and Ada Lovelace GPUs are well suited to business requirements when the model fits in their memory and the workload uses formats accelerated by these generations. This most commonly applies to the following scenarios:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Models in FP16 and BF16 formats<\/strong> \u2013 both formats are accelerated by the Tensor Cores in all three generations. If the company does not plan to quantise to FP8 or FP4, the Ampere generation will support the same models.<\/li>\n\n\n\n<li><strong>Inference in FP8<\/strong> \u2013 the Ada Lovelace generation provides hardware support for FP8. This generation also includes energy-efficient server accelerators designed for inference and video processing.<\/li>\n\n\n\n<li><strong>Expanding existing infrastructure<\/strong> \u2013 purchasing cards from the same generation as those already in use makes it easier to maintain consistent drivers, system images and procedures.<\/li>\n\n\n\n<li><strong>Connecting two GPUs in a workstation via an NVLink bridge<\/strong> \u2013 among workstation GPUs, this is supported by higher-end RTX A series models (Ampere).<\/li>\n\n\n\n<li><strong>Virtualisation and mixed workloads<\/strong> \u2013 Ada Lovelace server accelerators combine inference with virtual workstations and video processing.<\/li>\n\n\n\n<li><strong>Compact systems<\/strong> \u2013 low-profile Ampere and Ada Lovelace GPUs fit into small enclosures and are well suited to lighter models.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Before purchasing, check whether the AI libraries and frameworks in use support the chosen generation in the required CUDA version.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Same_generation_different_editions_%E2%80%93_using_the_RTX_PRO_6000_Blackwell_as_an_example\"><\/span><strong>Same generation, different editions \u2013 using the RTX PRO 6000 Blackwell as an example<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Within a single generation, the same GPU model may be available in several editions. <strong>The RTX PRO 6000 Blackwell is available in three editions: Workstation Edition, Max-Q Workstation Edition and Server Edition.<\/strong> All use the same GB202 chip and have 96 GB of GDDR7 memory. They differ in cooling, power limit and intended installation environment.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Specification<\/strong><\/th><th><strong>Workstation Edition<\/strong><\/th><th><strong>Max-Q Workstation Edition<\/strong><\/th><th><strong>Server Edition<\/strong><\/th><\/tr><\/thead><tbody><tr><td>Cooling<\/td><td>active, 2 fans<\/td><td>active, 1 fan<\/td><td>passive (heatsink)<\/td><\/tr><tr><td>Power consumption<\/td><td>up to 600 W<\/td><td>300 W<\/td><td>up to 600 W (configurable)<\/td><\/tr><tr><td>Card length<\/td><td>304,8 mm<\/td><td>266,7 mm<\/td><td>266,7 mm<\/td><\/tr><tr><td>Memory bandwidth<\/td><td>1792 GB\/s<\/td><td>1792 GB\/s<\/td><td>1597 GB\/s<\/td><\/tr><tr><td>Video outputs<\/td><td>4 \u00d7 DisplayPort 2.1b<\/td><td>4 \u00d7 DisplayPort 2.1b<\/td><td>4 \u00d7 DisplayPort 2.1<\/td><\/tr><tr><td>Deployment environment<\/td><td>workstation<\/td><td>workstation, including multi-GPU configurations<\/td><td>server with forced-air cooling<\/td><\/tr><tr><td>Example product<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/900-5G144-2500-000\" target=\"_blank\" rel=\"noopener noreferrer\">NVIDIA 900-5G144-2500-000<\/a><\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/900-5G153-2500-000\" target=\"_blank\" rel=\"noopener noreferrer\">NVIDIA 900-5G153-2500-000<\/a><\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/TCSRTXPRO6000SE-BLK\" target=\"_blank\" rel=\"noopener noreferrer\">PNY TCSRTXPRO6000SE-BLK<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Workstation Edition<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Workstation Edition operates at the full 600 W power limit and has two dedicated fans to dissipate heat from the card. It is designed for single-GPU workstations where maximum single-GPU performance is the priority.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Max-Q Workstation Edition<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Max-Q Workstation Edition operates at a 300 W power limit, half that of the Workstation Edition, while offering the same memory capacity and bandwidth. Lower power consumption means less heat to remove from the enclosure. Up to four cards in this edition can be installed in a single workstation, providing a total of 384 GB of GPU memory.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Server Edition<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Server Edition has no fans. Heat is dissipated by the airflow generated by the server fans, so this edition is designed for servers with forced-air cooling rather than workstation enclosures. These cards are optimised for continuous operation in data centres and support, among other capabilities, secure boot and confidential computing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same edition split is also available for other RTX PRO Blackwell series models.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"GPU_generation_and_edition_by_AI_workload\"><\/span><strong>GPU generation and edition by AI workload<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Choosing a GPU starts with the workload it must handle: prototyping on a single system has different requirements from inference for an entire team or training large models. The table below matches common business AI workloads with the GPU generation and edition that meet their requirements.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>AI workload<\/strong><\/th><th><strong>Generation<\/strong><\/th><th><strong>Edition or form factor<\/strong><\/th><th><strong>Example products<\/strong><\/th><\/tr><\/thead><tbody><tr><td>Prototyping and testing smaller models on a single system<\/td><td>Ada Lovelace or Blackwell<\/td><td>workstation, compact GPU<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTXPRO4000LP-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX PRO 4000 SFF Blackwell<\/a><\/td><\/tr><tr><td>Models in FP16 or BF16, expanding workstations with Ampere GPUs<\/td><td>Ampere<\/td><td>workstation<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/AV8J4AA\" target=\"_blank\" rel=\"noopener noreferrer\">RTX A1000<\/a><\/td><\/tr><tr><td>Local language model for a team, inference in FP8<\/td><td>Ada Lovelace<\/td><td>workstation<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTX6000ADA-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX 6000 Ada<\/a><\/td><\/tr><tr><td>Large model on a single GPU, FP4 quantisation<\/td><td>Blackwell<\/td><td>Workstation Edition<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTXPRO6000-SB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX PRO 6000 Blackwell Workstation Edition<\/a><\/td><\/tr><tr><td>Multiple GPUs in a single workstation<\/td><td>Blackwell<\/td><td>Max-Q Workstation Edition<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/VCNRTXPRO6000MQ-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX PRO 6000 Blackwell Max-Q<\/a><\/td><\/tr><tr><td>Low-power server inference, video processing<\/td><td>Ada Lovelace<\/td><td>low-profile server accelerator<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/TCSL4PCIE-PB\" target=\"_blank\" rel=\"noopener noreferrer\">L4<\/a><\/td><\/tr><tr><td>Multimodal inference in a server, partitioning a GPU between teams (MIG)<\/td><td>Blackwell<\/td><td>Server Edition<\/td><td><a href=\"https:\/\/www.senetic.co.uk\/product\/TCSRTXPRO6000SE-PB\" target=\"_blank\" rel=\"noopener noreferrer\">RTX PRO 6000 Blackwell Server Edition<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We cover selecting an entire system in separate articles: <a href=\"https:\/\/www.senetic.co.uk\/blog\/1615,which-computer-for-ai-should-you-choose-types-specifications-and-applications\/\" target=\"_blank\" rel=\"noopener noreferrer\">how to choose a computer for AI<\/a>,<a href=\"https:\/\/www.senetic.co.uk\/blog\/1656,which-ai-server-should-you-choose-three-investment-levels-and-real-costs\/\" target=\"_blank\" rel=\"noopener noreferrer\"> a server for AI<\/a> and <a href=\"https:\/\/www.senetic.co.uk\/blog\/1703,local-llms-in-business-when-it-pays-to-run-ai-on-your-own-hardware\/\" target=\"_blank\" rel=\"noopener noreferrer\">when to run a language model on your own hardware<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_asked_questions_about_GPU_generations_for_AI\"><\/span><strong>Frequently asked questions about GPU generations for AI<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-17621793875159382\"><strong class=\"schema-faq-question\">Does a newer GPU generation always deliver higher performance for AI workloads?<\/strong> <p class=\"schema-faq-answer\">A newer generation offers higher peak performance and support for lower precisions, but the benefit depends on the workload. If a model runs in FP16 or BF16 and fits in GPU memory, GPUs from all three generations (Ampere, Ada Lovelace and Blackwell) accelerate it in hardware. The greatest difference is seen with models quantised to FP8 or FP4, and with models that require large amounts of memory.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159383\"><strong class=\"schema-faq-question\">What is the difference between RTX A6000, RTX 6000 Ada and RTX PRO 6000 Blackwell?<\/strong> <p class=\"schema-faq-answer\">These are GPUs in the same class from three successive generations. RTX A6000 (Ampere) and RTX 6000 Ada each have 48 GB of GDDR6 memory and a power draw of 300 W. RTX PRO 6000 Blackwell has 96 GB of GDDR7 memory with bandwidth of 1792 GB\/s and is the only one of the three to support FP4.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159384\"><strong class=\"schema-faq-question\">Is an Ampere-generation GPU suitable for AI workloads?<\/strong> <p class=\"schema-faq-answer\">Yes. Ampere-generation GPUs accelerate computations in the TF32, BF16 and FP16 formats, which are used for training and inference across many models. They do not provide hardware acceleration for the FP8 format, which is supported by the Ada Lovelace and Blackwell generations.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159385\"><strong class=\"schema-faq-question\">What does computational precision mean in a GPU?<\/strong> <p class=\"schema-faq-answer\">Precision is the format used to store a model&#8217;s numbers, such as FP16, FP8 or FP4. The fewer the bits, the less memory the model requires and the faster it runs. Output quality at lower precision depends on the quantisation method used.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159386\"><strong class=\"schema-faq-question\">What does support for FP4 in the Blackwell generation mean?<\/strong> <p class=\"schema-faq-answer\">FP4 is a 4-bit numerical format in which a model occupies less than half the memory required by FP16. Fifth-generation Tensor Cores process FP4 at twice the throughput of FP8. This makes it possible to run a larger model on a single GPU or accelerate inference.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159387\"><strong class=\"schema-faq-question\">How can you identify a GPU generation from its model name?<\/strong> <p class=\"schema-faq-answer\">Workstation GPUs labelled RTX A belong to the Ampere generation, GPUs with the Ada suffix belong to Ada Lovelace, and the RTX PRO series with the Blackwell suffix belongs to the Blackwell generation. For server accelerators, the first letter of the name indicates the generation: A for Ampere and L for Ada Lovelace.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159388\"><strong class=\"schema-faq-question\">What is the difference between RTX PRO 6000 Blackwell Workstation Edition, Max-Q and Server Edition?<\/strong> <p class=\"schema-faq-answer\">All three editions use the same chip and have 96 GB of GDDR7 memory. Workstation Edition operates at up to 600 W with active cooling, Max-Q at 300 W with active cooling, while Server Edition uses passive cooling designed for servers with forced airflow.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-17621793875159389\"><strong class=\"schema-faq-question\">Can a single GPU be shared between multiple users?<\/strong> <p class=\"schema-faq-answer\">Yes, if the GPU supports MIG (Multi-Instance GPU) technology. RTX PRO 6000 Blackwell can be partitioned into up to four isolated instances of 24 GB each, with dedicated memory and compute cores.<\/p> <\/div> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>The GPU generation determines which numerical formats (precisions) the processor handles AI workloads fastest, its memory bandwidth and the card&#8217;s power requirements. Three generations coexist in the NVIDIA professional GPU portfolio: Ampere (e.g. RTX A6000), Ada Lovelace (e.g. RTX 6000 Ada) and Blackwell (e.g. RTX PRO 6000 Blackwell). Each successive generation supports lower compute precisions<\/p>\n","protected":false},"author":8,"featured_media":1745,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_meta-robots-adv":"","_yoast_wpseo_bctitle":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","footnotes":""},"categories":[141,163],"tags":[],"class_list":["post-1746","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-emerging-technologies","category-hardware-technologies"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>NVIDIA GPU generations for enterprise AI workloads | Senetic<\/title>\n<meta name=\"description\" content=\"Compare NVIDIA Ampere, Ada Lovelace and Blackwell GPUs for enterprise AI. 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I create content that helps businesses choose the right hardware and software without overspending or guesswork. I find my balance on the yoga mat, in the mountains and on my mountain bike.\",\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/author\\\/aleksandra\\\/\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159382\",\"position\":1,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159382\",\"name\":\"Czy nowsza generacja karty GPU zawsze oznacza wy\u017csz\u0105 wydajno\u015b\u0107 w zadaniach AI?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Nowsza generacja oferuje wy\u017csz\u0105 wydajno\u015b\u0107 szczytow\u0105 i obs\u0142ug\u0119 ni\u017cszych precyzji, ale korzy\u015b\u0107 zale\u017cy od zadania. Je\u015bli model dzia\u0142a w FP16 lub BF16 i mie\u015bci si\u0119 w pami\u0119ci karty, karty GPU wszystkich trzech generacji (Ampere, Ada Lovelace, Blackwell) przyspieszaj\u0105 go sprz\u0119towo. Najwi\u0119ksz\u0105 r\u00f3\u017cnic\u0119 wida\u0107 przy modelach kwantyzowanych do FP8 lub FP4 oraz przy modelach wymagaj\u0105cych du\u017cej pami\u0119ci.\",\"inLanguage\":\"en-GB\"},\"inLanguage\":\"en-GB\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159383\",\"position\":2,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159383\",\"name\":\"Czym r\u00f3\u017cni\u0105 si\u0119 RTX A6000, RTX 6000 Ada i RTX PRO 6000 Blackwell?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"To karty GPU tej samej klasy z trzech kolejnych generacji. RTX A6000 (Ampere) i RTX 6000 Ada maj\u0105 po 48 GB pami\u0119ci GDDR6 i pob\u00f3r mocy 300 W. RTX PRO 6000 Blackwell ma 96 GB pami\u0119ci GDDR7 o przepustowo\u015bci 1792 GB\\\/s i jako jedyna z nich obs\u0142uguje FP4.\",\"inLanguage\":\"en-GB\"},\"inLanguage\":\"en-GB\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159384\",\"position\":3,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159384\",\"name\":\"Czy karta GPU generacji Ampere nadaje si\u0119 do pracy z AI?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Tak. Karty GPU generacji Ampere przyspieszaj\u0105 obliczenia w formatach TF32, BF16 i FP16, stosowanych przy trenowaniu i inferencji wielu modeli. Nie przyspieszaj\u0105 sprz\u0119towo formatu FP8, kt\u00f3ry obs\u0142uguj\u0105 generacje Ada Lovelace i Blackwell.\",\"inLanguage\":\"en-GB\"},\"inLanguage\":\"en-GB\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159385\",\"position\":4,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159385\",\"name\":\"Czym jest precyzja oblicze\u0144 w karcie GPU?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Precyzja to format, w jakim zapisywane s\u0105 liczby modelu, np. FP16, FP8 czy FP4. Im mniej bit\u00f3w, tym mniej pami\u0119ci zajmuje model i tym szybciej dzia\u0142a. Jako\u015b\u0107 wynik\u00f3w przy ni\u017cszej precyzji zale\u017cy od zastosowanej metody kwantyzacji.\",\"inLanguage\":\"en-GB\"},\"inLanguage\":\"en-GB\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159386\",\"position\":5,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159386\",\"name\":\"Co oznacza obs\u0142uga FP4 w generacji Blackwell?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"FP4 to 4-bitowy format liczb, w kt\u00f3rym model zajmuje mniej ni\u017c po\u0142ow\u0119 pami\u0119ci potrzebnej w FP16. Rdzenie Tensor pi\u0105tej generacji przetwarzaj\u0105 FP4 z dwukrotnie wy\u017csz\u0105 przepustowo\u015bci\u0105 ni\u017c FP8. Pozwala to uruchomi\u0107 wi\u0119kszy model na jednej karcie GPU lub przyspieszy\u0107 inferencj\u0119.\",\"inLanguage\":\"en-GB\"},\"inLanguage\":\"en-GB\"},{\"@type\":\"Question\",\"@id\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159387\",\"position\":6,\"url\":\"https:\\\/\\\/www.senetic.co.uk\\\/blog\\\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\\\/#faq-question-17621793875159387\",\"name\":\"Jak rozpozna\u0107 generacj\u0119 karty GPU po nazwie modelu?\",\"answerCount\":1,\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Karty do stacji roboczych oznaczone jako RTX A nale\u017c\u0105 do generacji Ampere, karty z dopiskiem Ada \u2013 do Ada Lovelace, a seria RTX PRO z dopiskiem Blackwell \u2013 do generacji Blackwell. 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I create content that helps businesses choose the right hardware and software without overspending or guesswork. I find my balance on the yoga mat, in the mountains and on my mountain bike.","url":"https:\/\/www.senetic.co.uk\/blog\/author\/aleksandra\/"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159382","position":1,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159382","name":"Czy nowsza generacja karty GPU zawsze oznacza wy\u017csz\u0105 wydajno\u015b\u0107 w zadaniach AI?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Nowsza generacja oferuje wy\u017csz\u0105 wydajno\u015b\u0107 szczytow\u0105 i obs\u0142ug\u0119 ni\u017cszych precyzji, ale korzy\u015b\u0107 zale\u017cy od zadania. Je\u015bli model dzia\u0142a w FP16 lub BF16 i mie\u015bci si\u0119 w pami\u0119ci karty, karty GPU wszystkich trzech generacji (Ampere, Ada Lovelace, Blackwell) przyspieszaj\u0105 go sprz\u0119towo. Najwi\u0119ksz\u0105 r\u00f3\u017cnic\u0119 wida\u0107 przy modelach kwantyzowanych do FP8 lub FP4 oraz przy modelach wymagaj\u0105cych du\u017cej pami\u0119ci.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159383","position":2,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159383","name":"Czym r\u00f3\u017cni\u0105 si\u0119 RTX A6000, RTX 6000 Ada i RTX PRO 6000 Blackwell?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"To karty GPU tej samej klasy z trzech kolejnych generacji. RTX A6000 (Ampere) i RTX 6000 Ada maj\u0105 po 48 GB pami\u0119ci GDDR6 i pob\u00f3r mocy 300 W. RTX PRO 6000 Blackwell ma 96 GB pami\u0119ci GDDR7 o przepustowo\u015bci 1792 GB\/s i jako jedyna z nich obs\u0142uguje FP4.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159384","position":3,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159384","name":"Czy karta GPU generacji Ampere nadaje si\u0119 do pracy z AI?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Tak. Karty GPU generacji Ampere przyspieszaj\u0105 obliczenia w formatach TF32, BF16 i FP16, stosowanych przy trenowaniu i inferencji wielu modeli. Nie przyspieszaj\u0105 sprz\u0119towo formatu FP8, kt\u00f3ry obs\u0142uguj\u0105 generacje Ada Lovelace i Blackwell.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159385","position":4,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159385","name":"Czym jest precyzja oblicze\u0144 w karcie GPU?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Precyzja to format, w jakim zapisywane s\u0105 liczby modelu, np. FP16, FP8 czy FP4. Im mniej bit\u00f3w, tym mniej pami\u0119ci zajmuje model i tym szybciej dzia\u0142a. Jako\u015b\u0107 wynik\u00f3w przy ni\u017cszej precyzji zale\u017cy od zastosowanej metody kwantyzacji.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159386","position":5,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159386","name":"Co oznacza obs\u0142uga FP4 w generacji Blackwell?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"FP4 to 4-bitowy format liczb, w kt\u00f3rym model zajmuje mniej ni\u017c po\u0142ow\u0119 pami\u0119ci potrzebnej w FP16. Rdzenie Tensor pi\u0105tej generacji przetwarzaj\u0105 FP4 z dwukrotnie wy\u017csz\u0105 przepustowo\u015bci\u0105 ni\u017c FP8. Pozwala to uruchomi\u0107 wi\u0119kszy model na jednej karcie GPU lub przyspieszy\u0107 inferencj\u0119.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159387","position":6,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159387","name":"Jak rozpozna\u0107 generacj\u0119 karty GPU po nazwie modelu?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Karty do stacji roboczych oznaczone jako RTX A nale\u017c\u0105 do generacji Ampere, karty z dopiskiem Ada \u2013 do Ada Lovelace, a seria RTX PRO z dopiskiem Blackwell \u2013 do generacji Blackwell. W akceleratorach serwerowych litera na pocz\u0105tku nazwy wskazuje generacj\u0119: A \u2013 Ampere, L \u2013 Ada Lovelace.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159388","position":7,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159388","name":"Czym r\u00f3\u017cni si\u0119 RTX PRO 6000 Blackwell Workstation Edition od Max-Q i Server Edition?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Wszystkie trzy edycje maj\u0105 ten sam uk\u0142ad i 96 GB pami\u0119ci GDDR7. Workstation Edition pracuje z poborem mocy do 600 W i ch\u0142odzeniem aktywnym, Max-Q z poborem mocy 300 W i ch\u0142odzeniem aktywnym, a Server Edition z ch\u0142odzeniem pasywnym, przeznaczonym do serwer\u00f3w z wymuszonym obiegiem powietrza.","inLanguage":"en-GB"},"inLanguage":"en-GB"},{"@type":"Question","@id":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159389","position":8,"url":"https:\/\/www.senetic.co.uk\/blog\/1746,blackwell-ada-or-ampere-choosing-a-gpu-generation-for-enterprise-ai\/#faq-question-17621793875159389","name":"Czy jedn\u0105 kart\u0119 GPU mo\u017cna podzieli\u0107 mi\u0119dzy kilku u\u017cytkownik\u00f3w?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Tak, je\u015bli karta obs\u0142uguje technologi\u0119 MIG (Multi-Instance GPU). RTX PRO 6000 Blackwell mo\u017cna podzieli\u0107 na maksymalnie cztery odizolowane instancje po 24 GB, z kt\u00f3rych ka\u017cda ma w\u0142asn\u0105 pami\u0119\u0107 i rdzenie obliczeniowe.","inLanguage":"en-GB"},"inLanguage":"en-GB"}]}},"_links":{"self":[{"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/posts\/1746","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/comments?post=1746"}],"version-history":[{"count":2,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/posts\/1746\/revisions"}],"predecessor-version":[{"id":1748,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/posts\/1746\/revisions\/1748"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/media\/1745"}],"wp:attachment":[{"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/media?parent=1746"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/categories?post=1746"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.senetic.co.uk\/blog\/wp-json\/wp\/v2\/tags?post=1746"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}