{"id":1102,"date":"2026-06-15T18:14:18","date_gmt":"2026-06-15T18:14:18","guid":{"rendered":"https:\/\/convly.ai\/best-mini-pc-for-local-ai-2026\/"},"modified":"2026-06-15T18:17:52","modified_gmt":"2026-06-15T18:17:52","slug":"best-mini-pc-for-local-ai-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/","title":{"rendered":"Best Mini PCs for Local AI in 2026: A Buyer&#8217;s Guide"},"content":{"rendered":"<p>Two years ago, running a capable language model at home meant a tower stuffed with two or three GPUs, a 1,000-watt power supply, and a fan profile that sounded like a hairdryer. In 2026 you can do most of the same work from a box that fits in your palm and sips power like a laptop. The catch is that the mini-PC market has fragmented into machines that look similar but behave very differently once a model is loaded.<\/p>\n<p>This guide cuts through that. We compare the four classes of small-form-factor machine that actually matter for local AI right now \u2014 Apple&#8217;s Mac mini, NVIDIA&#8217;s DGX Spark, AMD&#8217;s Ryzen AI Max+ (&#8220;Strix Halo&#8221;) boxes, and Intel&#8217;s NPU-equipped mini PCs \u2014 with verified specs, current prices, and real token-per-second numbers. By the end you&#8217;ll know which one fits the models you want to run, and which spec sheet lines are marketing rather than performance.<\/p>\n<div class=\"convly-tldr\">\n<h3>\u0627\u0644\u0648\u062c\u0628\u0627\u062a \u0627\u0644\u0631\u0626\u064a\u0633\u064a\u0629<\/h3>\n<ul>\n<li><strong>Memory capacity decides what fits; memory bandwidth decides how fast it runs.<\/strong> Both numbers matter, and the marketing usually only highlights one.<\/li>\n<li><strong>The Mac mini M4 Pro (~$1,999, 48GB) is the best all-rounder<\/strong> for most people: silent, ~30W under load, and comfortable up to ~32B-parameter models. Note that the 64GB tier was pulled amid the 2026 memory shortage, so 48GB is the practical ceiling today.<\/li>\n<li><strong>NVIDIA&#8217;s DGX Spark ($3,999 at launch, $4,699 after a Feb 2026 hike) holds 128GB<\/strong> and crushes prompt processing, but its 273 GB\/s bandwidth caps token generation at roughly 38 tok\/s on a 120B model in standardized testing.<\/li>\n<li><strong>AMD Strix Halo mini PCs (from ~$1,500) match the Spark&#8217;s generation speed for a fraction of the price<\/strong> thanks to the same unified-memory trick, but lag badly on prompt processing.<\/li>\n<li><strong>Intel mini PCs are for small models and NPU offload, not 70B-class work<\/strong> \u2014 useful, cheap, but a different category.<\/li>\n<li><strong>No mini PC beats a multi-GPU desktop on raw generation speed.<\/strong> You buy these for size, silence, power, and large unified memory \u2014 not peak throughput.<\/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-6a3077151b5b1\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">\u062a\u0628\u062f\u064a\u0644<\/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-6a3077151b5b1\"  aria-label=\"\u062a\u0628\u062f\u064a\u0644\" \/><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\/ar\/best-mini-pc-for-local-ai-2026\/#Unified_memory_vs_VRAM_the_one_concept_that_explains_everything\" >Unified memory vs VRAM: the one concept that explains everything<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#Apple_Mac_mini_M4_M4_Pro_the_default_pick\" >Apple Mac mini (M4 \/ M4 Pro): the default pick<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#NVIDIA_DGX_Spark_128GB_and_a_CUDA_stack_at_a_price\" >NVIDIA DGX Spark: 128GB and a CUDA stack, at a price<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#AMD_Ryzen_AI_Max_395_Strix_Halo_the_value_play\" >AMD Ryzen AI Max+ 395 (Strix Halo): the value play<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#Intel_mini_PCs_small_models_and_NPU_offload\" >Intel mini PCs: small models and NPU offload<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#The_comparison_table\" >The comparison table<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#Recommendations_by_use_case\" >Recommendations by use case<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#FAQ\" >\u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#Bottom_line\" >\u062e\u0644\u0627\u0635\u0629 \u0627\u0644\u0642\u0648\u0644<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/ar\/best-mini-pc-for-local-ai-2026\/#Related_articles\" >\u0645\u0642\u0627\u0644\u0627\u062a \u0630\u0627\u062a \u0635\u0644\u0629<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Unified_memory_vs_VRAM_the_one_concept_that_explains_everything\"><\/span>Unified memory vs VRAM: the one concept that explains everything<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Every recommendation below hinges on this distinction, so it&#8217;s worth thirty seconds.<\/p>\n<p>A traditional GPU has its own dedicated VRAM. An RTX 4090 has 24GB; if your model and its context don&#8217;t fit in 24GB, it won&#8217;t run on that card, full stop. VRAM is fast \u2014 the 4090&#8217;s is about 1,008 GB\/s \u2014 but there&#8217;s never much of it relative to model sizes.<\/p>\n<p><strong>\u0627\u0644\u0630\u0627\u0643\u0631\u0629 \u0627\u0644\u0645\u0648\u062d\u062f\u0629<\/strong> flips the trade. Apple&#8217;s M-series, NVIDIA&#8217;s GB10, and AMD&#8217;s Strix Halo all share a single pool of memory between CPU and GPU, so a 128GB machine can dedicate 96GB-plus to a model. That&#8217;s how a palm-sized box runs a 120-billion-parameter model that won&#8217;t fit on any single consumer GPU. The price you pay is bandwidth: unified LPDDR5x runs at roughly 120\u2013275 GB\/s, a fraction of discrete VRAM. And because token generation is memory-bandwidth-bound, that ceiling sets your tokens-per-second \u2014 no matter how much compute the chip claims. Hold those two numbers (capacity and bandwidth) in mind and every spec sheet below becomes readable. If you want the full GPU-side picture, see our companion piece on the <a href=\"\/ar\/best-gpus-for-local-llms-2026\/\">\u0623\u0641\u0636\u0644 \u0648\u062d\u062f\u0627\u062a \u0645\u0639\u0627\u0644\u062c\u0629 \u0627\u0644\u0631\u0633\u0648\u0645\u064a\u0627\u062a \u0644\u0644\u0645\u0627\u062c\u0633\u062a\u064a\u0631 \u0627\u0644\u0645\u062d\u0644\u064a \u0641\u064a \u0639\u0627\u0645 2026<\/a>.<\/p>\n<p>One note on the benchmark model we lean on below: gpt-oss-120B is a mixture-of-experts model with about 117B total parameters but only ~5.1B active per token. That MoE design is exactly why a quantized version fits \u2014 and runs at usable speeds \u2014 on these unified-memory boxes despite their modest bandwidth.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Apple_Mac_mini_M4_M4_Pro_the_default_pick\"><\/span>Apple Mac mini (M4 \/ M4 Pro): the default pick<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The Mac mini remains the easiest recommendation for the largest number of people, and 2026 pricing changes only sharpened that. After Apple dropped the 256GB storage tier in May 2026, the base M4 mini now starts at $799 (16GB unified memory, 512GB SSD), with a 24GB memory option available.<\/p>\n<p>The base M4 has a 10-core GPU and 120 GB\/s of bandwidth \u2014 fine for 8B-class models, where it turns out around 18\u201322 tok\/s on Llama 3.2 8B at Q4. For serious local AI you want the <strong>M4 Pro<\/strong>, which steps up to a 16- or 20-core GPU and, crucially, <strong>273 GB\/s of bandwidth<\/strong> \u2014 more than double the base chip. Configured with 48GB (around $1,999), it comfortably holds a 32B model quantized to Q4 entirely in memory, running Qwen 2.5 32B in the 10\u201315 tok\/s range.<\/p>\n<p>A caveat worth knowing before you buy: the M4 Pro&#8217;s spec sheet supports up to 64GB, but Apple pulled the 64GB configuration from sale during the 2026 DRAM shortage, and as of mid-2026 the highest reliably orderable tier is 48GB. If you specifically need more than that, the 128GB unified-memory boxes below are the realistic path.<\/p>\n<p>What sells the Mac mini isn&#8217;t peak speed \u2014 it&#8217;s the whole package. It draws roughly 15W idle and around 30W under inference load, and the fan barely audibly spins up. You can leave one running as an always-on inference server on a shelf and forget it exists. Software support via Ollama, LM Studio, and Apple&#8217;s MLX framework is excellent. If you&#8217;re new to local models, our <a href=\"\/ar\/what-is-ollama-complete-guide-2026\/\">complete guide to Ollama<\/a> walks through getting a model running on exactly this kind of machine.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Pros<\/h4>\n<ul>\n<li>Effectively silent; 15\u201330W power draw<\/li>\n<li>Best-in-class software ecosystem (MLX, Ollama, LM Studio)<\/li>\n<li>M4 Pro&#8217;s 273 GB\/s bandwidth is strong for the size and price<\/li>\n<li>Resale value and build quality are excellent<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Cons<\/h4>\n<ul>\n<li>Practically caps at 48GB today (64GB tier pulled in the 2026 shortage) \u2014 can&#8217;t touch 70B+ models the 128GB boxes run<\/li>\n<li>Unified memory is soldered; buy the capacity you&#8217;ll need up front<\/li>\n<li>No NVIDIA CUDA path, which matters for some training\/fine-tuning tooling<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"NVIDIA_DGX_Spark_128GB_and_a_CUDA_stack_at_a_price\"><\/span>NVIDIA DGX Spark: 128GB and a CUDA stack, at a price<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The DGX Spark (unveiled as &#8220;Project DIGITS&#8221; at CES 2025, renamed DGX Spark at GTC in March 2025, and shipping October 15, 2025) is NVIDIA&#8217;s bid to put a &#8220;personal AI supercomputer&#8221; on your desk. It pairs a GB10 Grace Blackwell superchip \u2014 a 20-core Arm CPU (10\u00d7 Cortex-X925 + 10\u00d7 Cortex-A725) plus a Blackwell GPU \u2014 with <strong>128GB of coherent unified LPDDR5x<\/strong>, a 4TB self-encrypting SSD, and a ConnectX-7 200 Gbps NIC for linking two units. NVIDIA rates it at up to 1 petaflop of FP4 AI performance, and per NVIDIA it can run inference on models up to ~200B parameters, or fine-tune up to ~70B. Power is supplied by a 240W adapter.<\/p>\n<p>Here&#8217;s the honest part. The Spark is a prompt-processing monster: on gpt-oss-120B it pushes about <strong>1,723 tok\/s of prefill<\/strong> in standardized testing, in the league of a triple-RTX-3090 rig. But token <em>generation<\/em> lands at just <strong>~38.6 tok\/s<\/strong> in the same comparison, because the GB10&#8217;s memory bandwidth is only <strong>273 \u062c\u064a\u062c\u0627\u0628\u0627\u064a\u062a\/\u062b\u0627\u0646\u064a\u0629<\/strong> \u2014 the same as a Mac mini M4 Pro, and the binding constraint during the memory-bound decode phase. Heavily optimized inference stacks (vLLM, SGLang, NVIDIA&#8217;s own TensorRT-LLM) have reportedly pushed single-unit gpt-oss-120B generation up toward 50\u201360 tok\/s with the right config, but the bandwidth ceiling that keeps it well below a multi-GPU rig is physics, not software.<\/p>\n<p>Then there&#8217;s price. The Spark launched at $3,999 and rose to <strong>$4,699<\/strong> in February 2026 amid memory-supply constraints \u2014 an 18% jump NVIDIA attributed to DRAM and NAND shortages. That&#8217;s roughly $37 per GB of memory \u2014 and for pure generation throughput, a trio of used RTX 3090s reportedly costs less and runs several times faster. The Spark earns its keep if you specifically need the CUDA\/NVIDIA software stack, NVFP4, or the 200B-parameter inference headroom in a 240W box. We dig deeper in our <a href=\"\/ar\/nvidia-digits-personal-ai-computer-review\/\">DGX Spark \/ Project DIGITS review<\/a> and the head-to-head <a href=\"\/ar\/nvidia-digits-vs-mac-studio-for-local-ai\/\">DGX Spark vs Mac Studio<\/a> comparison.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"AMD_Ryzen_AI_Max_395_Strix_Halo_the_value_play\"><\/span>AMD Ryzen AI Max+ 395 (Strix Halo): the value play<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>AMD&#8217;s Strix Halo is the surprise of this generation. The flagship <strong>Ryzen AI Max+ 395<\/strong> packs 16 Zen 5 cores, a 40-compute-unit RDNA 3.5 GPU (the Radeon 8060S), and a 50-TOPS XDNA 2 NPU. Paired with up to <strong>128GB of LPDDR5X<\/strong>, of which up to 96GB can be handed to the GPU, it does the same unified-memory trick as the Spark \u2014 run a 120B model that no single consumer GPU can hold \u2014 at a fraction of the cost.<\/p>\n<p>The trade is bandwidth and prompt processing. Strix Halo&#8217;s memory tops out around 256 GB\/s, and on gpt-oss-120B it manages only <strong>~340 tok\/s of prefill<\/strong> versus the Spark&#8217;s 1,723. But here&#8217;s the kicker: token generation is <strong>~34 tok\/s<\/strong>, within a whisker of the Spark&#8217;s 38. For chat-style workloads where you generate more than you ingest, the gap is small. Expect roughly 12 tok\/s on Llama 3.3 70B at Q4, at 80\u2013120W.<\/p>\n<p>What makes it compelling is price and choice. Entry pricing has climbed with the 2026 RAM shortage: 64GB versions of the GMKtec EVO-X2 sell from around $1,500, while the 128GB EVO-X2 now runs closer to $2,200. Framework&#8217;s repairable Desktop starts around $1,639 (barebones, Ryzen AI Max+ 395, 64GB) before storage and OS, and Corsair&#8217;s AI Workstation 300 launched near $2,000 but has swung well past that during the shortage. Software is the catch \u2014 ROCm and llama.cpp work well, but the ecosystem is rougher than Apple&#8217;s or NVIDIA&#8217;s, and Windows AI features lean on the NPU rather than the big GPU.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Pros<\/h4>\n<ul>\n<li>128GB unified memory configs available \u2014 the cheapest path to 70B+ models<\/li>\n<li>Generation speed nearly matches the DGX Spark for a fraction of the price<\/li>\n<li>Open x86 platform; runs Windows or Linux, broad app compatibility<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Cons<\/h4>\n<ul>\n<li>Weak prompt processing \u2014 long-context\/RAG workloads feel slow<\/li>\n<li>ROCm tooling is less polished than CUDA or MLX<\/li>\n<li>Soldered memory; 2026 RAM prices inflated street pricing<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Intel_mini_PCs_small_models_and_NPU_offload\"><\/span>Intel mini PCs: small models and NPU offload<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Intel&#8217;s mini PCs occupy a different tier, and it&#8217;s important not to mis-buy here. Current Arrow Lake-H chips like the Core Ultra 9 285H pair an Arc iGPU with a 13-TOPS NPU, for up to roughly 99 TOPS of platform AI compute when you count the CPU and GPU; Intel&#8217;s IPEX-LLM stack brings Ollama and llama.cpp to its iGPU and NPU. The bigger 2026 step is Panther Lake (Core Ultra Series 3), launched at CES 2026, which pairs a 50-TOPS NPU with a much stronger GPU for up to ~180 platform TOPS \u2014 though it remains a laptop\/mobile-class platform, not a desktop big-model machine.<\/p>\n<p>But none of these are unified-memory big-model machines. With standard DDR5 (typically ~120 GB\/s dual-channel) and no 96GB-to-GPU allocation, an Intel mini PC is the right tool for 3B\u20138B models, on-device assistants, transcription, and NPU-accelerated background tasks \u2014 not for running a 70B model. If your workload is &#8220;a quantized 8B model and some Windows AI features,&#8221; an Intel box is cheap and power-efficient. If it&#8217;s &#8220;the biggest model I can fit,&#8221; look at the unified-memory machines above. The NPU-versus-GPU trade-off here is its own topic, covered in our <a href=\"\/ar\/npu-vs-gpu-for-ai-2026\/\">NPU vs GPU for AI<\/a> breakdown.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_comparison_table\"><\/span>The comparison table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Generation figures below are for gpt-oss-120B (token generation \/ prompt processing) from standardized llama.cpp-style testing where both fit; smaller-model notes are given otherwise. Prices are mid-2026, USD, and move with the ongoing memory shortage.<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Machine<\/th>\n<th>\u0627\u0644\u0630\u0627\u0643\u0631\u0629 \u0627\u0644\u0645\u0648\u062d\u062f\u0629<\/th>\n<th>\u0639\u0631\u0636 \u0627\u0644\u0646\u0637\u0627\u0642 \u0627\u0644\u062a\u0631\u062f\u062f\u064a<\/th>\n<th>Realistic model ceiling<\/th>\n<th>Gen \/ prefill (120B)<\/th>\n<th>\u0627\u0644\u0637\u0627\u0642\u0629<\/th>\n<th>Price (2026)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mac mini M4 (base)<\/td>\n<td>16\u201332GB<\/td>\n<td>120 \u062c\u064a\u062c\u0627\u0628\u0627\u064a\u062a\/\u062b\u0627\u0646\u064a\u0629<\/td>\n<td>~8\u201314B (Q4)<\/td>\n<td>n\/a (8B: ~20 tok\/s)<\/td>\n<td>~30W<\/td>\n<td>$799+<\/td>\n<\/tr>\n<tr>\n<td>Mac mini M4 Pro<\/td>\n<td>up to 48GB*<\/td>\n<td>273 \u062c\u064a\u062c\u0627\u0628\u0627\u064a\u062a\/\u062b\u0627\u0646\u064a\u0629<\/td>\n<td>~32B (Q4)<\/td>\n<td>n\/a (32B: 10\u201315 tok\/s)<\/td>\n<td>~30W<\/td>\n<td>~$1,999<\/td>\n<\/tr>\n<tr>\n<td>AMD Strix Halo (Ryzen AI Max+ 395)<\/td>\n<td>up to 128GB<\/td>\n<td>~256 GB\/s<\/td>\n<td>~120B (Q4 MoE)<\/td>\n<td>34 \/ 340 tok\/s<\/td>\n<td>80\u2013120W<\/td>\n<td>$1,500\u20133,000+<\/td>\n<\/tr>\n<tr>\n<td>NVIDIA DGX Spark<\/td>\n<td>128GB<\/td>\n<td>273 \u062c\u064a\u062c\u0627\u0628\u0627\u064a\u062a\/\u062b\u0627\u0646\u064a\u0629<\/td>\n<td>~200B (inference)<\/td>\n<td>39 \/ 1,723 tok\/s<\/td>\n<td>~240W max<\/td>\n<td>$3,999\u20134,699<\/td>\n<\/tr>\n<tr>\n<td>Intel Arrow Lake-H mini PC<\/td>\n<td>DDR5 (no big GPU pool)<\/td>\n<td>~120 GB\/s<\/td>\n<td>~8B (Q4)<\/td>\n<td>\u063a\u064a\u0631 \u0645\u062a\u0627\u062d<\/td>\n<td>~65W<\/td>\n<td>$600\u20131,200<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><em>*The M4 Pro&#8217;s spec sheet supports 64GB, but that tier was pulled from sale during the 2026 DRAM shortage; 48GB is the practical ceiling in mid-2026.<\/em><\/p>\n<p>For reference, a Mac Studio M3 Ultra runs about 819 GB\/s of bandwidth (and decodes the same 120B model around 70 tok\/s), while a triple-3090 rig hits ~124 tok\/s decode \u2014 both well outside mini-PC territory, and a reminder of what you trade away for the small footprint. If you&#8217;re weighing a larger Apple box, our <a href=\"\/ar\/mac-studio-m4-max-vs-m4-ultra-for-ai\/\">Mac Studio M4 Max vs M4 Ultra<\/a> guide covers that step up.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Recommendations_by_use_case\"><\/span>Recommendations by use case<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Most people \/ silent always-on assistant:<\/strong> Mac mini M4 Pro, 48GB. The best balance of capability, near-zero noise, ~30W power, and a mature software stack. Drop to the 24GB base M4 if you only need 8B models.<\/p>\n<p><strong>Maximum model size on a budget:<\/strong> an AMD Strix Halo box (GMKtec EVO-X2, Framework Desktop, or Corsair AI Workstation 300). 128GB lets you load 70B\u2013120B models the Mac mini can&#8217;t touch, at generation speeds that nearly match the far pricier Spark \u2014 and even with shortage-inflated pricing, it&#8217;s well below the Spark.<\/p>\n<p><strong>CUDA development \/ NVIDIA workflow \/ heavy prompt processing:<\/strong> DGX Spark. You&#8217;re paying a premium for the NVIDIA stack, NVFP4, ConnectX clustering, and best-in-class prefill \u2014 justified only if those specifically matter to you.<\/p>\n<p><strong>Small on-device models and NPU tasks:<\/strong> an Intel Arrow Lake or Panther Lake mini PC. Cheap, efficient, and right-sized for 8B-class work and Windows AI features.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>\u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What&#8217;s the best mini PC for running local LLMs in 2026?<\/h3>\n<p>For most users it&#8217;s the Mac mini M4 Pro with 48GB \u2014 quiet, low-power, and capable up to ~32B models. If you need to run 70B+ models, an AMD Strix Halo box with 128GB is the value choice, and the NVIDIA DGX Spark is the premium CUDA option.<\/p>\n<h3>How much RAM do I need to run a 70B-parameter model?<\/h3>\n<p>A 70B model quantized to Q4 needs roughly 40\u201348GB just for weights, plus headroom for context. In practice you want a 64GB machine at minimum, and 128GB to run it comfortably with a large context window. That rules out the current 48GB Mac mini for the largest 70B setups and points to the 128GB unified-memory boxes.<\/p>\n<h3>Why is the NVIDIA DGX Spark slow at generating tokens despite costing $4,000+?<\/h3>\n<p>Because token generation is limited by memory bandwidth, and the Spark&#8217;s 273 GB\/s is modest \u2014 the same as a Mac mini M4 Pro. Its strength is prompt processing (about 1,723 tok\/s on a 120B model) and 128GB of capacity, not raw generation speed, where standardized tests put it around 38 tok\/s (optimized stacks can reach ~50\u201360).<\/p>\n<h3>Is unified memory as good as a dedicated GPU&#8217;s VRAM?<\/h3>\n<p>It&#8217;s a trade. Unified memory gives you far more capacity (up to 128GB) so you can run models that won&#8217;t fit on any single consumer GPU, but at much lower bandwidth than VRAM. For large models that won&#8217;t fit otherwise, it&#8217;s the only practical option; for smaller models a discrete GPU is faster.<\/p>\n<h3>Can a Mac mini run a 70B model?<\/h3>\n<p>Not really, anymore. With the 64GB tier pulled in the 2026 shortage, the top Mac mini M4 Pro you can buy has 48GB \u2014 enough for a tight, heavily quantized 70B at best, with practical headroom topping out around 32B at Q4. For 70B work, step up to a 128GB machine like a Strix Halo box, a Mac Studio, or the DGX Spark.<\/p>\n<h3>Are AMD Strix Halo mini PCs good for AI, or is the software too rough?<\/h3>\n<p>They&#8217;re genuinely capable \u2014 128GB of memory and generation speeds near the DGX Spark for a fraction of the price. The caveat is software: ROCm and llama.cpp work but are less polished than Apple&#8217;s MLX or NVIDIA&#8217;s CUDA, and prompt processing is weak. If you&#8217;re comfortable with some setup, the value is excellent.<\/p>\n<h3>How much power and noise should I expect from these machines?<\/h3>\n<p>The Mac mini is the quietest and most efficient, at ~30W under load and effectively silent. Strix Halo boxes draw 80\u2013120W with audible but modest fans. The DGX Spark ships with a 240W power adapter. All are dramatically quieter and lower-power than a multi-GPU desktop, which can pull 300\u2013450W or more.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>\u062e\u0644\u0627\u0635\u0629 \u0627\u0644\u0642\u0648\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The mini-PC era for local AI is real, but the marketing oversells it in one specific way: these machines win on size, silence, power, and large unified memory \u2014 not on raw speed. No box here beats a multi-GPU desktop on tokens per second, and you shouldn&#8217;t buy one expecting that.<\/p>\n<p>Pick by the model size you actually run. For 8B\u201332B models with the least fuss, the Mac mini M4 Pro is the easy call and the one we&#8217;d recommend to most readers. To run 70B\u2013120B models without a tower, an AMD Strix Halo box delivers the best capability per dollar, with the DGX Spark reserved for those who specifically need NVIDIA&#8217;s stack and prompt-processing muscle. And if your needs stop at 8B models, an Intel mini PC will do the job for less. Match the memory to the model, read the bandwidth line, and ignore the petaflop on the box.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>\u0645\u0642\u0627\u0644\u0627\u062a \u0630\u0627\u062a \u0635\u0644\u0629<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/ar\/rtx-50-super-for-ai-2026\/\">RTX 5080 Super &amp; 5070 Super for AI: What the Leaked VRAM Upgrades Mean for Local LLMs (2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/ar\/nvidia-vera-rubin-explained-2026\/\">NVIDIA Vera Rubin Explained: The Next-Gen AI Platform That Cuts Inference Costs 10\u00d7 (2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/ar\/rx-9070-xt-vs-rtx-5080-for-ai-2026\/\">AMD RX 9070 XT vs RTX 5080 for AI in 2026: Can AMD Punch Above Its Price?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/ar\/rx-9070-xt-vs-rtx-5070-ti-for-ai-2026\/\">AMD RX 9070 XT vs RTX 5070 Ti for AI in 2026: Does ROCm Close the Gap?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>A no-hype buyer&#8217;s guide to small-form-factor machines for local LLMs in 2026 \u2014 Apple&#8217;s Mac mini, NVIDIA&#8217;s DGX Spark, AMD Strix Halo boxes and Intel \u2014 with verified specs, prices and token-per-second numbers, plus picks by use case.<\/p>","protected":false},"author":1,"featured_media":1112,"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 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