{"id":1108,"date":"2026-06-15T18:14:26","date_gmt":"2026-06-15T18:14:26","guid":{"rendered":"https:\/\/convly.ai\/npu-vs-gpu-for-ai-2026\/"},"modified":"2026-06-15T18:17:44","modified_gmt":"2026-06-15T18:17:44","slug":"npu-vs-gpu-for-ai-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/","title":{"rendered":"NPU vs GPU for AI: What&#8217;s the Difference? (2026)"},"content":{"rendered":"<p>Every laptop, phone and graphics card sold in 2026 now advertises an &#8220;AI&#8221; number. Some quote TOPS, some quote TFLOPS, and the marketing rarely explains that those are different units measuring different chips doing different work. The NPU in your new laptop and the GPU in your desktop are both technically &#8220;AI accelerators,&#8221; but they were designed to win at opposite ends of the same problem.<\/p>\n<p>This piece sorts out what an NPU actually is, how it differs from a GPU at the architecture level, and which one matters for what you are trying to do. We use real, verified numbers from the silicon shipping right now: Apple&#8217;s Neural Engine, Qualcomm&#8217;s Hexagon, the Intel and AMD NPUs inside Copilot+ PCs, and NVIDIA&#8217;s RTX and Blackwell data-center parts. No theoretical chips, no hype.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>Different jobs, not better-or-worse.<\/strong> NPUs are built for low-power, always-on inference on a device; GPUs are built for raw parallel throughput and for training.<\/li>\n<li><strong>TOPS and TFLOPS are not the same unit.<\/strong> NPUs are rated in INT8 TOPS; GPUs are usually quoted in floating-point TFLOPS. You cannot compare the two numbers directly.<\/li>\n<li><strong>The scale gap is enormous.<\/strong> A 2026 laptop NPU lands around 45-80 TOPS. An NVIDIA RTX 5090 is rated at 3,352 AI TOPS, and a data-center B200 reaches roughly 4,500 TFLOPS in FP8.<\/li>\n<li><strong>NPUs win on efficiency, not speed.<\/strong> They run background AI (camera, transcription, Copilot features) at a fraction of a GPU&#8217;s wattage, which is why every Copilot+ PC needs 40+ TOPS of NPU.<\/li>\n<li><strong>For local LLMs today, the GPU (and memory bandwidth) still wins.<\/strong> NPU software support is immature; a 7B model on a Snapdragon NPU runs around 9-12 tokens\/second in mid-2026, while a discrete GPU is far faster.<\/li>\n<li><strong>The line is blurring.<\/strong> Apple&#8217;s M5 puts neural accelerators inside every GPU core, and AMD&#8217;s Strix Halo pairs a 50-TOPS NPU with 128GB of unified memory to run 120B-parameter models locally.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a307c6f8a0f0\" 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: #000000;color:#000000\" 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: #000000;color:#000000\" 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-6a307c6f8a0f0\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#What_an_NPU_actually_is\" >What an NPU actually is<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#How_a_GPU_differs_architecturally\" >How a GPU differs architecturally<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#TOPS_vs_TFLOPS_why_the_numbers_dont_line_up\" >TOPS vs TFLOPS: why the numbers don&#8217;t line up<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#Where_each_one_wins\" >Where each one wins<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#The_2026_chips_by_the_numbers\" >The 2026 chips, by the numbers<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#What_this_means_for_running_AI_locally\" >What this means for running AI locally<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#A_quick_word_on_CPUs_and_TPUs\" >A quick word on CPUs and TPUs<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/npu-vs-gpu-for-ai-2026\/#Related_articles\" >Articles connexes<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_an_NPU_actually_is\"><\/span>What an NPU actually is<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>An NPU, or Neural Processing Unit, is a chip block built to do one narrow thing extremely efficiently: the multiply-accumulate math at the heart of neural networks. It is not a general-purpose processor. It cannot run your operating system or a game. What it can do is push enormous volumes of low-precision integer math (typically INT8 or INT4) through dedicated hardware at very low power.<\/p>\n<p>That efficiency is the entire point. An NPU exists so your phone can blur a video background, transcribe a voice memo, or run a small language model without draining the battery or spinning up a fan. On Windows, Microsoft made this an explicit hardware class: a <a href=\"\/fr\/snapdragon-x-elite-vs-apple-m4-ai-laptops\/\">Copilot+ PC<\/a> requires an NPU capable of more than 40 trillion operations per second (40+ TOPS), specifically so on-device features like live captions and image generation run on the NPU instead of the CPU or GPU. Windows 11 now schedules AI work across CPU, GPU and NPU and even shows NPU utilization in Task Manager.<\/p>\n<p>The key word is <em>d\u00e9duction<\/em>. NPUs run already-trained models. They are almost never used to train models from scratch, which is a fundamentally different and far heavier workload.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_a_GPU_differs_architecturally\"><\/span>How a GPU differs architecturally<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A GPU started life rendering triangles, and that legacy shaped it into a massively parallel floating-point engine with thousands of cores. Modern GPUs added Tensor Cores (NVIDIA&#8217;s term) dedicated to matrix math, which is what makes them the default tool for AI. An RTX 5090 has 21,760 CUDA cores plus fifth-generation Tensor Cores on top.<\/p>\n<p>Three architectural differences matter:<\/p>\n<ul>\n<li><strong>Precision.<\/strong> GPUs are happy doing high-precision floating point (FP16, FP32) needed for training, and recent ones add lower-precision tiers. Blackwell GPUs are the first consumer cards to support FP4. NPUs lean almost entirely on low-precision integer math, which is great for inference but unsuitable for training.<\/li>\n<li><strong>Memory.<\/strong> This is the quiet differentiator. A GPU has its own fast, dedicated VRAM (the RTX 5090 ships 32GB of GDDR7 at roughly 1.79 TB\/s). An NPU shares the system&#8217;s main memory with everything else, which caps how large a model it can hold and how fast it can feed it.<\/li>\n<li><strong>Power.<\/strong> An RTX 5090 draws up to 575W. A laptop NPU runs the same class of inference inside a few watts. That single fact explains why both chips exist.<\/li>\n<\/ul>\n<p>If you want to run large models locally, memory and bandwidth often matter more than raw compute, which is exactly why GPU buyers obsess over VRAM. Our guide to the <a href=\"\/fr\/best-gpus-for-local-llms-2026\/\">les meilleurs GPU pour les LLM locaux<\/a> goes deep on that trade-off.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"TOPS_vs_TFLOPS_why_the_numbers_dont_line_up\"><\/span>TOPS vs TFLOPS: why the numbers don&#8217;t line up<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is where most spec sheets mislead. TOPS counts trillions of <em>operations<\/em> per second, and on NPUs it almost always means INT8 integer operations. TFLOPS counts trillions of <em>floating-point<\/em> operations per second, the unit used for GPUs and for training. They are not interchangeable.<\/p>\n<p>INT8 roughly doubles throughput versus FP16 on the same hardware, so a vendor can publish a bigger headline number simply by quoting the lower-precision format. That is why TOPS ratings tend to be INT8: it looks better. Both figures are also peak theoretical numbers measured under ideal conditions, not sustained real-world throughput.<\/p>\n<p>There is a second trap: platform TOPS versus NPU-only TOPS. Intel&#8217;s Lunar Lake, for example, is marketed as 120 &#8220;platform&#8221; TOPS, but that bundles 67 TOPS from the GPU, 48 from the NPU and 5 from the CPU. The NPU on its own is 48 TOPS. When you compare chips, make sure you are comparing the same block.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_each_one_wins\"><\/span>Where each one wins<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>NPU is the right tool when\u2026<\/h4>\n<ul>\n<li>The workload is always-on or background (camera effects, noise suppression, live captions, Windows Studio Effects).<\/li>\n<li>Battery life and thermals are the priority, on a phone or thin laptop.<\/li>\n<li>You are running small, quantized models built for the device.<\/li>\n<li>You want AI features without a fan ever turning on.<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>GPU is the right tool when\u2026<\/h4>\n<ul>\n<li>You are training or fine-tuning a model.<\/li>\n<li>You want to run large local LLMs (13B, 30B, 70B+) at usable speeds.<\/li>\n<li>You need raw throughput for image, video or 3D generation.<\/li>\n<li>You are serving models to many users at once in a data center.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<p>A clean mental model: the NPU handles the AI you don&#8217;t think about, and the GPU handles the AI you deliberately sit down to run. Most 2026 laptops ship both, and Windows decides which to use per task.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_2026_chips_by_the_numbers\"><\/span>The 2026 chips, by the numbers<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here is where the real silicon lands. All figures below are verified against vendor and primary sources as of mid-2026. Note the units carefully: the first group is NPU INT8 TOPS, the second is GPU AI compute.<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Chip<\/th>\n<th>Class<\/th>\n<th>AI accelerator rating<\/th>\n<th>Where it lives<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Apple A18 \/ A18 Pro Neural Engine<\/td>\n<td>Phone NPU<\/td>\n<td>35 TOPS (16-core)<\/td>\n<td>iPhone 16 series<\/td>\n<\/tr>\n<tr>\n<td>Apple M4 Neural Engine<\/td>\n<td>Laptop NPU<\/td>\n<td>38 TOPS<\/td>\n<td>MacBook Air\/Pro<\/td>\n<\/tr>\n<tr>\n<td>Qualcomm Snapdragon X Elite (Hexagon)<\/td>\n<td>Laptop NPU<\/td>\n<td>45 TOPS<\/td>\n<td>Copilot+ PCs (wave 1)<\/td>\n<\/tr>\n<tr>\n<td>Intel Core Ultra 200V (Lunar Lake)<\/td>\n<td>Laptop NPU<\/td>\n<td>48 TOPS<\/td>\n<td>Copilot+ PCs<\/td>\n<\/tr>\n<tr>\n<td>AMD Ryzen AI 300 (XDNA 2)<\/td>\n<td>Laptop NPU<\/td>\n<td>50 TOPS<\/td>\n<td>Copilot+ PCs<\/td>\n<\/tr>\n<tr>\n<td>Qualcomm Snapdragon X2 Elite (Hexagon)<\/td>\n<td>Laptop NPU<\/td>\n<td>80 TOPS (up to 85 on top SKUs)<\/td>\n<td>Copilot+ PCs (2026 wave)<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA RTX 5080<\/td>\n<td>Consumer GPU<\/td>\n<td>1,801 AI TOPS<\/td>\n<td>Desktop \/ workstation<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA RTX 5090<\/td>\n<td>Consumer GPU<\/td>\n<td>3,352 AI TOPS<\/td>\n<td>Desktop \/ workstation<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA H100<\/td>\n<td>Data-center GPU<\/td>\n<td>1,979 TFLOPS (FP8 dense)<\/td>\n<td>Cloud \/ servers<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA B200 (Blackwell)<\/td>\n<td>Data-center GPU<\/td>\n<td>~4,500 TFLOPS FP8 dense (9,000 FP4)<\/td>\n<td>Cloud \/ servers<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The jump from the NPU rows to the GPU rows is not a typo. A flagship laptop NPU at 80 TOPS and an RTX 5090 at 3,352 AI TOPS are roughly two orders of magnitude apart, before you even account for the GPU&#8217;s 32GB of dedicated high-bandwidth memory. That gap is the whole story: NPUs were never trying to win on absolute performance. They win on performance per watt.<\/p>\n<h3>Apple&#8217;s approach is shifting<\/h3>\n<p>Apple is worth a separate note because it stopped playing the TOPS game. The M4 Neural Engine was rated at 38 TOPS, but for the M5 (which shipped in the 14-inch MacBook Pro in late 2025, with M5 Pro and M5 Max following in March 2026) Apple did not publish a Neural Engine TOPS figure at all. Instead it redesigned the GPU to put a Neural Accelerator inside each of the 10 GPU cores, and claims up to 3.5x faster AI performance than M4. That is a deliberate bet that GPU-integrated AI matters more than a standalone NPU number, and it muddies the tidy NPU-vs-GPU split. If you are weighing Mac against Windows for AI, the <a href=\"\/fr\/snapdragon-x-elite-vs-apple-m4-ai-laptops\/\">Snapdragon X Elite vs Apple M4 comparison<\/a> breaks down how the two ecosystems actually feel in use.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_this_means_for_running_AI_locally\"><\/span>What this means for running AI locally<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here is the honest part the spec sheets skip. In mid-2026, the NPU is still the weakest link for running local LLMs, not because the silicon is slow but because the software stack is immature. Independent testing of a Snapdragon X Elite running a quantized 7B model through Qualcomm&#8217;s QNN path lands around 9-12 tokens per second. Smooth, tool-like interaction starts closer to 30 tokens per second. Worse, popular runtimes like Ollama still have no NPU compute backend, so on many machines that powerful NPU sits idle while the CPU does the work.<\/p>\n<p>So if your goal today is to actually run a sizeable model at home, a discrete GPU with plenty of VRAM remains the practical answer, and AMD&#8217;s software stack has matured enough to be a genuine alternative worth weighing in our <a href=\"\/fr\/amd-rocm-vs-nvidia-cuda-2026\/\">ROCm vs CUDA breakdown<\/a>. The interesting middle ground is unified-memory designs: AMD&#8217;s Ryzen AI Max (&#8220;Strix Halo&#8221;) pairs a 50-TOPS XDNA 2 NPU with up to 128GB of unified memory and can allocate up to 96GB as VRAM, enough to load 120B-parameter models locally. That is far more model than any 32GB GPU can hold, and it is reshaping the <a href=\"\/fr\/best-mini-pc-for-local-ai-2026\/\">mini-PC market for local AI<\/a>.<\/p>\n<p>For phones, the calculus is different and the NPU clearly wins: there is no GPU alternative sipping milliwatts, and on-device features are tuned to the NPU. If mobile AI is your priority, see the <a href=\"\/fr\/best-phones-for-on-device-ai-2026\/\">best phones for on-device AI<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"A_quick_word_on_CPUs_and_TPUs\"><\/span>A quick word on CPUs and TPUs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Two other acronyms round out the picture. The CPU can run AI but is the slowest option for it; in Lunar Lake the CPU contributes just 5 of the platform&#8217;s TOPS. It mostly orchestrates and handles the parts NPUs and GPUs can&#8217;t.<\/p>\n<p>TPUs (Tensor Processing Units) are Google&#8217;s custom ASICs, conceptually closer to a giant data-center NPU than to a GPU. They live in the cloud, not in your devices. Google&#8217;s seventh-generation &#8220;Ironwood&#8221; TPU delivers 4,614 FP8 TFLOPS per chip with 192GB of HBM3e, and a full pod scales to thousands of chips for training and serving frontier models. You will never have one on your desk, but a lot of the AI you use is served from them.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is an NPU faster than a GPU?<\/h3>\n<p>No, not in absolute terms. A high-end GPU like the RTX 5090 (3,352 AI TOPS) vastly out-computes any consumer NPU (45-80 TOPS). The NPU&#8217;s advantage is efficiency: it does AI work at a few watts instead of hundreds, which matters for battery life and always-on features, not for peak speed.<\/p>\n<h3>Can I run ChatGPT-style models on my NPU?<\/h3>\n<p>You can run small, quantized local models on a 40+ TOPS NPU, but in mid-2026 the experience is limited. A 7B model runs around 9-12 tokens per second on a Snapdragon NPU, and many runtimes can&#8217;t target the NPU at all yet. For a smooth large-model experience, a GPU with ample VRAM is still the better tool.<\/p>\n<h3>Why do laptops need a 40 TOPS NPU for Copilot+?<\/h3>\n<p>Microsoft set 40+ TOPS as the floor so on-device AI features (live captions, Studio Effects, Recall, image generation) run on the NPU rather than the CPU or GPU. That keeps these always-on features from hammering battery life, and it guarantees a baseline capability developers can target.<\/p>\n<h3>What is the difference between TOPS and TFLOPS?<\/h3>\n<p>TOPS measures trillions of integer operations per second (usually INT8) and is used for NPUs. TFLOPS measures trillions of floating-point operations per second and is used for GPUs and training. Because they use different precisions and units, you cannot directly compare a TOPS number to a TFLOPS number.<\/p>\n<h3>Does Apple&#8217;s M5 have an NPU?<\/h3>\n<p>Yes. The M5 has a 16-core Neural Engine, but Apple no longer publishes a TOPS figure for it. Apple instead added Neural Accelerators to every GPU core and claims up to 3.5x faster AI than the M4, signaling a shift toward GPU-integrated AI rather than a standalone NPU spec.<\/p>\n<h3>Is a TPU better than a GPU for AI?<\/h3>\n<p>For Google&#8217;s own large-scale training and inference, TPUs are highly competitive and cost-effective at pod scale. But TPUs are cloud-only ASICs you can&#8217;t buy for a PC, whereas GPUs are general-purpose and run anywhere. For most people the practical choice is NPU versus GPU, not TPU.<\/p>\n<h3>Will NPUs replace GPUs for AI?<\/h3>\n<p>Not for heavy workloads. NPUs are taking over efficient, on-device inference, and that footprint will keep growing. But training, large local models and high-throughput generation still need GPUs (or TPUs). The realistic 2026 picture is convergence, with NPUs, GPUs and unified-memory designs each owning a slice.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>NPU versus GPU is the wrong framing if you treat it as a contest. They are two answers to two different questions. If you want efficient, always-on AI that doesn&#8217;t touch your battery, the NPU is doing its job invisibly inside your phone and laptop, and the 2026 generation (80 TOPS on Snapdragon X2, 48-50 TOPS on Intel and AMD) is genuinely capable. If you want to train models, run large local LLMs, or generate media at speed, the GPU is still the only serious option, and nothing in the NPU world is close to an RTX 5090 or a B200 on raw throughput.<\/p>\n<p>The most interesting development is that the boundary is dissolving. Apple is folding neural acceleration into the GPU, AMD is giving NPUs GPU-class memory, and the software is slowly catching up. For now, buy for the workload: NPU for efficiency and ambient AI, GPU for power and local model size. Don&#8217;t let a single TOPS number on a sticker make the decision for you.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articles connexes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/fr\/ollama-vs-jan-2026\/\">Ollama vs Jan: Which Local AI App Wins in 2026?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/lm-studio-complete-guide-2026\/\">LM Studio: The Complete Guide (2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/what-is-ollama-complete-guide-2026\/\">What Is Ollama? The Complete Guide to Running LLMs Locally in 2026<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/ollama-vs-lm-studio-vs-vllm-vs-llama-cpp-2026\/\">Ollama vs LM Studio vs vLLM vs llama.cpp: Which Should You Use in 2026?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>An NPU and a GPU both run AI, but they are built for opposite jobs. Here is what separates them in 2026, with real TOPS and TFLOPS numbers from the chips actually shipping.<\/p>","protected":false},"author":1,"featured_media":1118,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[245],"tags":[757,332,756,256,362,360,278,758],"class_list":["post-1108","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-chips","tag-ai-chips","tag-copilot-pc","tag-gpu","tag-local-llm","tag-neural-engine","tag-npu","tag-on-device-ai","tag-tops"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=1108"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1108\/revisions"}],"predecessor-version":[{"id":1123,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1108\/revisions\/1123"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/1118"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}