{"title":"NVIDIA","description":"\u003cp\u003eNVIDIA-powered hardware from Jetson edge kits to DGX AI supercomputers, the full GPU compute stack, Agent One-ready.\u003c\/p\u003e","products":[{"product_id":"nvidia-jetson-orin-nano-super","title":"NVIDIA Jetson Orin Nano Super (8 GB)","description":"\u003cp\u003ePalm-sized edge AI developer kit featuring the NVIDIA Jetson Orin Nano Super module with a 6-core Arm Cortex-A78AE CPU, a 1,024-core Ampere GPU with 32 Tensor Cores, 8 GB of LPDDR5 memory at 102 GB\/s, and delivering up to 67 TOPS of INT8 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned for robotics, vision, and small-model inference at the edge, with JetPack 6 on Ubuntu, CUDA, TensorRT, and the NVIDIA Isaac and Metropolis SDKs preinstalled, and support for PyTorch, Ollama, and Jetson Generative AI Lab.\u003c\/p\u003e\u003cp\u003eFits in a pocket on a reference carrier board with four USB 3.2 ports, Gigabit Ethernet, a microSD slot and M.2 slots, draws 7 to 25 W, and runs a 3-billion-parameter model at 4-bit or an 8-billion-parameter model quantized with limited context.\u003c\/p\u003e\u003cp\u003e$249. NVIDIA fixed price; in stock.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-orin\/nano-super-developer-kit\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419709927640,"sku":"NV-JETSON-ONS","price":249.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_Jetson_Orin_Nano_Super_white.png?v=1790222954"},{"product_id":"jetson-agx-orin-64gb","title":"NVIDIA Jetson AGX Orin Developer Kit (64 GB)","description":"\u003cp\u003eEdge AI developer kit featuring the NVIDIA Jetson AGX Orin module with a 12-core Arm Cortex-A78AE CPU, a 2,048-core Ampere GPU with 64 Tensor Cores, two NVDLA accelerators, 64 GB of LPDDR5 memory at 204.8 GB\/s, 64 GB of eMMC, and delivering up to 275 TOPS of INT8 AI performance.\u003c\/p\u003e\n\u003cp\u003eDesigned for robotics, autonomous machines, and multi-model inference at the edge, with JetPack 6 on Ubuntu, CUDA, TensorRT, DeepStream, and the Isaac SDK preinstalled, and support for PyTorch and Ollama for language models.\u003c\/p\u003e\n\u003cp\u003eFits on a desk or in a vehicle at 110 × 110 mm, with 10 GbE, USB-C, PCIe Gen4 M.2 slots and a 40-pin header, configurable from 15 to 60 W, and runs a 32-billion-parameter model at 8-bit or a 70-billion-parameter model at 4-bit in 64 GB of memory.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/marketplace.nvidia.com\/en-us\/enterprise\/robotics-edge\/jetson-agx-orin-developer-kit\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419723034840,"sku":"NV-JETSON-AGXORIN64","price":1999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_Jetson_AGX_Orin_DevKit_white.png?v=1790222310"},{"product_id":"dell-pro-max-16-rtx-pro","title":"Dell Pro Max 16 Plus (RTX PRO 5000 24 GB, 128 GB RAM)","description":"\u003cp\u003e16-inch Dell Pro Max 16 Plus mobile workstation featuring an Intel Core Ultra 9 285HX processor with vPro, an NVIDIA RTX PRO 5000 Blackwell GPU with 24 GB of GDDR7 on a replaceable module, up to 128 GB of 6400 MT\/s CAMM2 memory, up to 4 TB of SSD storage across three M.2 slots, and a 16-inch WUXGA display or 3840 × 2400 tandem OLED at 120 Hz.\u003c\/p\u003e\u003cp\u003eDesigned for local inference, fine-tuning, and rendering on a certified professional GPU, with Windows 11 Pro, NVIDIA AI Enterprise and CUDA support, Intel OpenVINO across the iGPU and NPU, Thunderbolt 5, and Dell's DGFF graphics module that allows the GPU to be serviced or upgraded.\u003c\/p\u003e\u003cp\u003eCarries at from 2.55 kg with a bottom hatch for user-serviceable memory and storage, ISV certifications and enterprise manageability, and the ability to run a 32-billion-parameter model at 4-bit in GPU memory or larger models by spilling into system RAM.\u003c\/p\u003e\u003cp\u003eFrom $3,534 with integrated graphics; the RTX PRO 5000 Blackwell 24 GB is a $3,560 upgrade. Dell configurator pricing, September 2026. The RTX PRO 5000 is offered only on the Pro Max 16 Plus, not the standard Pro Max 16.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.dell.com\/en-us\/shop\/dell-laptops\/dell-pro-max-16-plus-laptop\/spd\/dell-pro-max-mb16250-laptop\" target=\"_blank\" rel=\"noopener\"\u003eDell\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":52419723067608,"sku":"DELL-MB16250","price":3534.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/Dell_Pro_Max_16_Plus_white.png?v=1790222489"},{"product_id":"hp-zbook-fury-g1i-18-rtx-pro-5000","title":"HP ZBook Fury G1i 18 (RTX PRO 5000 24 GB, 192 GB RAM)","description":"\u003cp\u003e18-inch desktop-replacement mobile workstation featuring an Intel Core Ultra 9 285HX processor with vPro, an NVIDIA RTX PRO 5000 Blackwell GPU with 24 GB of GDDR7 at up to 175 W, up to 192 GB of DDR5 memory across four SODIMM slots, up to 16 TB across four PCIe Gen5 SSDs with RAID, and an 18-inch 4K 120 Hz display.\u003c\/p\u003e\u003cp\u003eDesigned for local inference, fine-tuning, and rendering on a certified professional GPU, with Windows 11 Pro or Ubuntu, NVIDIA AI Enterprise and CUDA support, Intel OpenVINO across the iGPU and NPU, Thunderbolt 5, and HP Wolf Security with ISV certifications.\u003c\/p\u003e\u003cp\u003eCarries at roughly 3.6 kg with a tool-less service door for memory and storage, a 99 Wh battery and a 330 W adapter, and runs a 32-billion-parameter model at 4-bit in GPU memory or a 70-billion-parameter model at 4-bit by spilling into 192 GB of system RAM.\u003c\/p\u003e\u003cp\u003eFrom ~$8,000 with the RTX PRO 5000. HP configurator pricing; the 192 GB memory ceiling is the reason to pick it over the Dell.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.hp.com\/us-en\/workstations\/zbook-fury.html\" target=\"_blank\" rel=\"noopener\"\u003eHP\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"HP","offers":[{"title":"Default Title","offer_id":52419723985112,"sku":"HP-ZBOOKFURY-G1I-18","price":8000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/og-image-zbook-fury.jpg?v=1789409976"},{"product_id":"nvidia-dgx-spark","title":"NVIDIA DGX Spark (128 GB, 4 TB)","description":"\u003cp\u003eDesktop AI system built on the NVIDIA GB10 Grace Blackwell Superchip, with 128 GB of unified LPDDR5X memory, a 20-core Arm CPU, 4 TB of NVMe storage, and up to 1 PFLOPS of FP4 AI performance in a 150 mm square chassis.\u003c\/p\u003e\u003cp\u003eDesigned to run and fine-tune models locally at full precision, with DGX OS, NVIDIA's AI software stack, CUDA, and Ollama preinstalled, and the same containers that run on DGX Cloud. Two units link over ConnectX-7 at 200 Gb\/s to pool 256 GB of memory.\u003c\/p\u003e\u003cp\u003eDraws under 240 W from a standard wall outlet, with Wi-Fi 7, 10 GbE, and four USB-C ports, runs models up to 200 billion parameters on a single unit, and up to 405 billion on a linked pair.\u003c\/p\u003e\u003cp\u003e$4,699 Founders Edition, direct from NVIDIA. Up $700 from launch in February 2026; unchanged since June.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419725164760,"sku":"NV-DGX-SPARK","price":4699.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_DGX_Spark_white.png?v=1790222640"},{"product_id":"dual-dgx-spark-linked","title":"NVIDIA Dual DGX Spark, linked (256 GB, 8 TB)","description":"\u003cp\u003eTwo NVIDIA DGX Spark systems linked over their built-in ConnectX-7 SmartNICs at 200 Gb\/s, pooling 256 GB of unified LPDDR5X memory, 40 Arm CPU cores, 8 TB of NVMe storage, and up to 2 PFLOPS of FP4 AI performance across two 150 mm square chassis.\u003c\/p\u003e\n\u003cp\u003eDesigned to run and fine-tune models that exceed a single unit's memory, with DGX OS, NVIDIA's AI software stack, CUDA, and NCCL for multi-node inference preinstalled, and the same containers that run on DGX Cloud.\u003c\/p\u003e\n\u003cp\u003eSits on one desk drawing under 480 W from two standard outlets, with a single 400G QSFP112 direct-attach cable between the units, and runs models up to 405 billion parameters at FP4 across the pair.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419729490136,"sku":"NV-DGX-SPARK-X2","price":9398.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_DGX_Spark_white_96bd703b-b228-448e-ad77-f22c2a0cd314.png?v=1790222745"},{"product_id":"rtx-pro-5000-72gb-tower","title":"RTX PRO 5000 Tower (72 GB, 256 GB RAM)","description":"\u003cp\u003eFull-size desktop workstation built around a single NVIDIA RTX PRO 5000 Blackwell with 72 GB of GDDR7 ECC memory delivering up to 2,900 AI TOPS at FP4, paired with an AMD Threadripper PRO or Intel Xeon W processor, up to 256 GB of DDR5 ECC memory, PCIe Gen5 NVMe storage, and a 1,000 W or larger power supply.\u003c\/p\u003e\u003cp\u003eDesigned for local inference and fine-tuning on models that exceed consumer VRAM at a lower cost than the 96 GB card, with Windows 11 Pro or Ubuntu, NVIDIA AI Enterprise, CUDA, PyTorch, and Triton support, and ISV certifications for engineering and media workflows.\u003c\/p\u003e\u003cp\u003eFloor-standing on a standard outlet at roughly 600 W under sustained load, ECC memory throughout, and the ability to run a 70-billion-parameter model at 8-bit or a 120-billion-parameter model at 4-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003eFrom ~$10,000. The card alone lists at $9,310–9,999 (SHI, Scan); built systems from Puget, Lambda, Dell, HP, and Lenovo run $10,000–15,000.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-5000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419730079960,"sku":"BLD-RTXPRO5000-TOWER","price":10000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_RTX_PRO_5000_white.png?v=1790222931"},{"product_id":"rtx-pro-6000-blackwell-96gb-tower","title":"RTX PRO 6000 Blackwell Tower (96 GB, 256 GB RAM)","description":"\u003cp\u003eFull-size desktop workstation built around a single NVIDIA RTX PRO 6000 Blackwell Workstation Edition with 96 GB of GDDR7 ECC memory delivering up to 4,000 AI TOPS at FP4, paired with an AMD Threadripper PRO or Intel Xeon W processor, up to 256 GB of DDR5 ECC memory, PCIe Gen5 NVMe storage, and a 1,600 W power supply.\u003c\/p\u003e\u003cp\u003eDesigned for local inference and fine-tuning on the largest single-GPU memory available in a desktop, with Windows 11 Pro or Ubuntu, NVIDIA AI Enterprise, CUDA, PyTorch, and Triton support, ISV certifications, and a second PCIe x16 slot for a matching card.\u003c\/p\u003e\u003cp\u003eFloor-standing on a dedicated 15 A circuit at roughly 900 W under sustained load, ECC memory throughout, and the ability to run a 70-billion-parameter model at 8-bit or a 180-billion-parameter model at 4-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003eFrom ~$20,000. The card alone is $16,000 on NVIDIA's marketplace as of September 2026, up 87% since launch; built systems $20,000–25,000.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-6000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419731259608,"sku":"BLD-RTXPRO6000-TOWER","price":20000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_RTX_PRO_6000_white_1.png?v=1790223037"},{"product_id":"dual-rtx-pro-6000-threadripper-pro","title":"Dual RTX PRO 6000 Blackwell Tower (192 GB, 512 GB RAM)","description":"\u003cp\u003eFull-size desktop workstation built around two NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPUs with 192 GB of combined GDDR7 ECC memory delivering up to 8,000 AI TOPS at FP4, paired with an AMD Threadripper PRO 9000 WX processor with up to 96 cores, up to 512 GB of DDR5 ECC memory across eight channels, PCIe Gen5 NVMe storage, and a 2,000 W power supply.\u003c\/p\u003e\u003cp\u003eDesigned for local inference and fine-tuning on models that exceed a single GPU, with Windows 11 Pro or Ubuntu, NVIDIA AI Enterprise, CUDA, PyTorch, and Triton support, tensor parallelism across both cards over PCIe Gen5, and ISV certifications.\u003c\/p\u003e\u003cp\u003eFloor-standing on a dedicated 20 A circuit at roughly 1,600 W under sustained load, ECC memory throughout, and the ability to run a 120-billion-parameter model at 8-bit or a 405-billion-parameter model at 4-bit across both GPUs.\u003c\/p\u003e\u003cp\u003eFrom ~$40,000. Two cards alone are $32,000 at current NVIDIA pricing; built systems from Puget, Lambda Vector Pro, and Comino run $40,000–50,000.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-6000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419731882200,"sku":"BLD-2XRTXPRO6000-TOWER","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_RTX_PRO_6000_white.png?v=1790220967"},{"product_id":"dgx-b300-8-gpu-system","title":"NVIDIA DGX B300 (8× B300, 2,304 GB)","description":"\u003cp\u003e10U rackmount AI system featuring eight NVIDIA Blackwell Ultra B300 GPUs on an HGX B300 baseboard, equipped with 2.3 TB of HBM3e memory, dual Intel Xeon 6 processors, up to 4 TB of DDR5, eight ConnectX-8 SuperNICs at 800 Gb\/s, and delivering up to 144 PFLOPS of FP4 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train and serve trillion-parameter-class models on a single node as NVIDIA's own reference system, with fifth-generation NVLink and NVSwitch at 1.8 TB\/s between GPUs, DGX OS and NVIDIA AI Enterprise preinstalled, and Mission Control for fleet operations when scaled to SuperPOD.\u003c\/p\u003e\u003cp\u003eAir-cooled in a standard rack at roughly 14 kW of continuous draw on a three-phase feed, and able to run a 1-trillion-parameter model at FP8 or larger at FP4 entirely in GPU memory.\u003c\/p\u003e\u003cp\u003ePrice available on request. NVIDIA list has moved above $300,000; OEM 8× B200 boxes list at $490,000, so budget $500,000+.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/dgx-b300\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Request a quote","offer_id":52457679519960,"sku":"NV-DGX-B300","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA_DGX_B300_white.png?v=1790220786"},{"product_id":"nvidia-h100-cloud","title":"NVIDIA H100 (cloud, 80 GB)","description":"\u003cp\u003eRented NVIDIA H100 SXM GPU with 80 GB of HBM3 at 3.35 TB\/s, 989 TFLOPS of FP16 dense compute, and NVLink at 900 GB\/s when rented as an 8-GPU node.\u003c\/p\u003e\u003cp\u003eDesigned for bursty training and inference with no capital outlay, billed per GPU-hour from $1.38 per GPU-hour on Vast.ai and boutique clouds to $8 on hyperscalers; RunPod $1.99, and scalable from one GPU to hundreds in minutes with CUDA, PyTorch, and the NVIDIA container stack preinstalled by every provider.\u003c\/p\u003e\u003cp\u003eLives in someone else's data center: nothing to power, cool, or secure, and the trade is per-hour cost and data leaving your premises. Runs a 70-billion-parameter model at 8-bit on one GPU; 405 billion on eight.\u003c\/p\u003e\u003cp\u003e$1.38\/hr floor (Vast.ai), $1.99 RunPod, $2.30–3.10 median, $8 hyperscaler. Price verified September 2026.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/h100\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419734110424,"sku":"CLOUD-H100","price":1.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/H100inSR680aV3.png?v=1790220600"},{"product_id":"nvidia-h200-cloud","title":"NVIDIA H200 (cloud, 141 GB)","description":"\u003cp\u003eRented NVIDIA H200 SXM GPU with 141 GB of HBM3e at 4.8 TB\/s, 989 TFLOPS of FP16 dense compute, and NVLink at 900 GB\/s when rented as an 8-GPU node.\u003c\/p\u003e\u003cp\u003eDesigned for bursty training and inference with no capital outlay, billed per GPU-hour from $3.72 per GPU-hour on-demand on specialist clouds, with spot capacity near $1.00, and scalable from one GPU to hundreds in minutes with CUDA, PyTorch, and the NVIDIA container stack preinstalled by every provider.\u003c\/p\u003e\u003cp\u003eLives in someone else's data center: nothing to power, cool, or secure, and the trade is per-hour cost and data leaving your premises. Runs a 120-billion-parameter model at 8-bit on one GPU; 405 billion at FP8 on eight with full context.\u003c\/p\u003e\u003cp\u003e$3.72\/hr on-demand floor, $3.99 Jarvislabs, ~$1.00 spot. Price verified September 2026.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/h200\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419736830168,"sku":"CLOUD-H200","price":3.72,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/h220-1_1.webp?v=1790220541"},{"product_id":"nvidia-b200-cloud","title":"NVIDIA B200 (cloud, 180 GB)","description":"\u003cp\u003eRented NVIDIA B200 SXM GPU with 180 GB of HBM3e at 8 TB\/s, 2.25 PFLOPS of FP16 dense compute and native FP4, and fifth-generation NVLink at 1.8 TB\/s when rented as an 8-GPU node.\u003c\/p\u003e\u003cp\u003eDesigned for bursty training and inference with no capital outlay, billed per GPU-hour from $2.12 per GPU-hour on marketplace clouds to $14–16 on hyperscalers; Spheron spot $5.34, and scalable from one GPU to hundreds in minutes with CUDA, PyTorch, and the NVIDIA container stack preinstalled by every provider.\u003c\/p\u003e\u003cp\u003eLives in someone else's data center: nothing to power, cool, or secure, and the trade is per-hour cost and data leaving your premises. Runs a 180-billion-parameter model at 8-bit on one GPU; 1 trillion at FP4 on eight.\u003c\/p\u003e\u003cp\u003e$2.12\/hr floor, $5.34 spot, $6.04 boutique, $14–16 hyperscaler. Allocation, not price, is the constraint.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/hgx\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419737616600,"sku":"CLOUD-B200","price":2.12,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/hgx_b200_blackwell.jpg?v=1790220475"},{"product_id":"nvidia-b300-cloud","title":"NVIDIA B300 (cloud, 288 GB)","description":"\u003cp\u003eRented NVIDIA B300 SXM GPU with 288 GB of HBM3e at 8 TB\/s, native FP4 at roughly 1.5× B200 throughput, and fifth-generation NVLink at 1.8 TB\/s when rented as an 8-GPU node.\u003c\/p\u003e\u003cp\u003eDesigned for bursty training and inference with no capital outlay, billed per GPU-hour from $3.67 per GPU-hour spot to $8.55 on-demand on early-access clouds, and scalable from one GPU to hundreds in minutes with CUDA, PyTorch, and the NVIDIA container stack preinstalled by every provider.\u003c\/p\u003e\u003cp\u003eLives in someone else's data center: nothing to power, cool, or secure, and the trade is per-hour cost and data leaving your premises. Runs a 288-billion-parameter model at 8-bit on one GPU; 1 trillion at FP8 on eight.\u003c\/p\u003e\u003cp\u003e$3.67\/hr spot, $8.55 on-demand, up to $18 on hyperscalers. Early availability; verified September 2026.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/hgx\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":52419738665176,"sku":"CLOUD-B300","price":3.67,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/hgx_b300_blackwell_ultra.jpg?v=1790220414"},{"product_id":"starlink-mini-jetson-field-node","title":"Starlink Mini + Jetson Field Node (8 GB, off-grid)","description":"\u003cp\u003ePortable off-grid AI node combining a Starlink Mini terminal with built-in Wi-Fi and roughly 100 Mb\/s downlink from a laptop-sized antenna, an NVIDIA Jetson Orin Nano Super with 8 GB of LPDDR5 and 67 TOPS of INT8 performance, and a 100 Wh USB-C power bank or solar panel.\u003c\/p\u003e\u003cp\u003eDesigned for field operations, remote sensing, and robotics anywhere with a view of the sky, with JetPack, CUDA, and TensorRT on the Jetson, local inference when the link drops and cloud fallback when it returns, and the whole kit fitting in a backpack.\u003c\/p\u003e\u003cp\u003eDraws roughly 40 W for the terminal and 7 to 25 W for the Jetson, runs for hours on a power bank or indefinitely on a 100 W panel, and runs a 3-billion-parameter model at 4-bit on device.\u003c\/p\u003e\u003cp\u003e~$1,000 as a kit: Starlink Mini ($599 hardware, service from $50\/month), Jetson Orin Nano Super ($249), power bank and enclosure. Reference bill of materials; assemble yourself.\u003c\/p\u003e","brand":"Hussh Build","offers":[{"title":"Default Title","offer_id":52419746332888,"sku":"BLD-STARLINK-JETSON","price":1000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/nvidia-jetson-orin-nano-super-8gb-developer-kit.jpg?v=1790220233"},{"product_id":"aethero-orbital-jetson","title":"Aethero Orbital Jetson (edge compute in LEO)","description":"\u003cp\u003eRadiation-tolerant edge computer built around the NVIDIA Jetson Orin module, hardened for low Earth orbit with shielding, error-correcting memory, and a watchdog architecture, and flown on orbit in 2025.\u003c\/p\u003e\u003cp\u003eDesigned to run AI inference on satellites for Earth observation, autonomy, and onboard data reduction so raw sensor data never has to be downlinked, with JetPack, CUDA, and TensorRT, and a ground-side toolchain for uploading models.\u003c\/p\u003e\u003cp\u003eLives on a spacecraft bus; sold to satellite operators and mission integrators, not to individuals. The trade is a flight-qualification schedule measured in months.\u003c\/p\u003e\u003cp\u003ePrice available on request. Mission and integration pricing from Aethero.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.aethero.com\/nxn\/\" target=\"_blank\" rel=\"noopener\"\u003eAethero\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Aethero","offers":[{"title":"Request a quote","offer_id":52457680273624,"sku":"AETH-ORBITAL-JETSON","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/Aethero_NxN-ECM_assembled_white.png?v=1790220100"},{"product_id":"lenovo-legion-pro-7i-gen-10-rtx-5090","title":"Lenovo Legion Pro 7i Gen 10 (RTX 5090 24 GB, 64 GB RAM)","description":"\u003cp\u003e16-inch portable AI workstation featuring an Intel Core Ultra 9 275HX processor, an NVIDIA GeForce RTX 5090 Laptop GPU with 24 GB of GDDR7 at up to 175 W, up to 64 GB of DDR5-6400 memory across two user-upgradable SODIMM slots, dual PCIe Gen4 M.2 slots for up to 4 TB, and a 2.5K 240 Hz OLED display.\u003c\/p\u003e\n\u003cp\u003eDesigned for local inference, fine-tuning, and agent development on the fastest GPU available in a laptop, with Windows 11 and full CUDA, PyTorch, and Ollama support, Thunderbolt 4 for external storage and displays, and Lenovo's Legion Coldfront vapor-chamber cooling to hold sustained GPU clocks under load.\u003c\/p\u003e\n\u003cp\u003eCarries in a backpack at roughly 2.7 kg with a 99.99 Wh battery for short sessions unplugged and a 400 W adapter for full performance, and runs a 32-billion-parameter model at 4-bit or a 13-billion-parameter model at 8-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.lenovo.com\/us\/en\/p\/laptops\/legion-laptops\/legion-pro-series\/legion-pro-7i-gen-10-16-inch-intel\/len101g0039\" target=\"_blank\" rel=\"noopener\"\u003eLenovo\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Lenovo","offers":[{"title":"Default Title","offer_id":52419870589144,"sku":"LEN-LEGIONPRO7I-G10-5090","price":2999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/6575387cv7d.jpg?v=1790218666"},{"product_id":"razer-blade-16-rtx-5090","title":"Razer Blade 16 (RTX 5090 24 GB, 64 GB RAM)","description":"\u003cp\u003e16-inch thin-and-light AI laptop featuring the AMD Ryzen AI 9 HX 370, an NVIDIA GeForce RTX 5090 Laptop GPU with 24 GB of GDDR7 at up to 160 W, up to 64 GB of DDR5-5600 memory, up to 4 TB across two M.2 slots, and a QHD+ 240 Hz OLED display.\u003c\/p\u003e\n\u003cp\u003eDesigned for local inference and fine-tuning on the fastest laptop GPU in a 15 mm chassis, with Windows 11 and full CUDA, PyTorch, and Ollama support, Thunderbolt 5 for external storage and displays, and a vapor chamber with Razer's thermal hood.\u003c\/p\u003e\n\u003cp\u003eCarries at 2.1 kg with a 90 Wh battery and a 280 W GaN adapter, machined from a single block of aluminum, and runs a 32-billion-parameter model at 4-bit or a 13-billion-parameter model at 8-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.razer.com\/gaming-laptops\/razer-blade-16-2025\" target=\"_blank\" rel=\"noopener\"\u003eRazer\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Razer","offers":[{"title":"Default Title","offer_id":52419899130072,"sku":"RZR-BLADE16-5090","price":4499.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/razer-blade-16-2024-product-photo.jpg?v=1790218532"},{"product_id":"msi-titan-18-hx","title":"MSI Titan 18 HX (RTX 5090 24 GB, 96 GB RAM)","description":"\u003cp\u003e18-inch desktop-replacement laptop featuring an Intel Core Ultra 9 285HX processor, an NVIDIA GeForce RTX 5090 Laptop GPU with 24 GB of GDDR7 at up to 175 W, up to 96 GB of DDR5-6400 memory, up to 6 TB across three M.2 slots including PCIe Gen5, and an 18-inch 4K mini-LED 120 Hz display.\u003c\/p\u003e\u003cp\u003eDesigned for local inference, fine-tuning, and content creation on the fastest laptop GPU with the most system memory in its class, with Windows 11, full CUDA, PyTorch, and Ollama support, Thunderbolt 5, and a Cherry mechanical keyboard.\u003c\/p\u003e\u003cp\u003eCarries at roughly 3.6 kg with a 99.9 Wh battery and a 400 W adapter, and runs a 32-billion-parameter model at 4-bit in GPU memory or a 70-billion-parameter model at 4-bit by spilling into 96 GB of system RAM.\u003c\/p\u003e\u003cp\u003e~$5,500. MSI configurator and retail; confirm at order.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.msi.com\/Laptop\/Titan-18-HX-AI-A2XWX\" target=\"_blank\" rel=\"noopener\"\u003eMSI\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"MSI","offers":[{"title":"Default Title","offer_id":52419902308568,"sku":"MSI-TITAN18HX","price":5499.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/1-7-e1741130573787.jpg?v=1790218421"},{"product_id":"framework-laptop-16","title":"Framework Laptop 16 (RTX 5070 8 GB, 96 GB RAM)","description":"\u003cp\u003e16-inch modular laptop featuring the AMD Ryzen AI 9 HX 370 with 12 Zen 5 cores and a 50 TOPS XDNA 2 NPU, an optional NVIDIA GeForce RTX 5070 Laptop graphics module with 8 GB of GDDR7, up to 96 GB of user-upgradable DDR5-5600 SODIMM memory, and up to 8 TB across two M.2 slots.\u003c\/p\u003e\u003cp\u003eDesigned for developers who want a repairable, upgradable machine, with Windows 11 or Linux, CUDA on the graphics module and ROCm on the integrated Radeon 890M, swappable expansion cards for ports, and a graphics module bay that can be upgraded as new modules ship.\u003c\/p\u003e\u003cp\u003eCarries at 2.1 kg with the graphics module and an 85 Wh battery, every part user-replaceable with a single screwdriver, and runs a 13-billion-parameter model at 4-bit on the graphics module or a 32-billion-parameter model at 4-bit in system memory.\u003c\/p\u003e\u003cp\u003e~$3,000 configured with the RTX 5070 module; ~$2,000 without. Framework configurator.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/frame.work\/laptop16\" target=\"_blank\" rel=\"noopener\"\u003eFramework\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Framework","offers":[{"title":"Default Title","offer_id":52419907420376,"sku":"FW-LAPTOP16-AI300","price":2999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/Screenshot_2026-09-18_at_2.28.46_PM.png?v=1789759735"},{"product_id":"acer-veriton-gn100","title":"Acer Veriton GN100 (128 GB, 4 TB)","description":"\u003cp\u003eUltra-compact personal AI workstation featuring the NVIDIA GB10 Grace Blackwell Superchip, equipped with 128 GB of unified LPDDR5X memory, a 20-core Arm CPU, 4 TB of NVMe storage, and delivering up to 1 PFLOPS of FP4 AI performance in a 150 mm square chassis.\u003c\/p\u003e\u003cp\u003eDesigned to work locally with large AI models, and scale to larger workloads by connecting two Acer Veriton GN100 systems with the NVIDIA ConnectX-7 SmartNIC, pre-installed with the latest NVIDIA AI software stack and support for common developer tools and frameworks, including PyTorch, Jupyter, and Ollama.\u003c\/p\u003e\u003cp\u003eSpace-efficient and secure, with Kensington lock support, Wi-Fi 7 and 10 GbE, a standard outlet at under 240 W, and the ability to link two units for handling AI models up to 405 billion parameters.\u003c\/p\u003e\u003cp\u003e$3,999.99 (DT.R6LAA.001). Staples, Best Buy, Connection — backordered at all three. The cheapest 4 TB GB10 box in the US on paper.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.acer.com\/us-en\/desktops-and-all-in-ones\/veriton-workstations\/veriton-gn100-ai-mini-workstation\" target=\"_blank\" rel=\"noopener\"\u003eAcer\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Acer","offers":[{"title":"Default Title","offer_id":52419929735384,"sku":"ACER-GN100","price":3999.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/c9d3b019-483e-474c-8bdf-15242ee46065.jpg?v=1789759505"},{"product_id":"asus-ascent-gx10","title":"ASUS Ascent GX10 (128 GB, 4 TB)","description":"\u003cp\u003eUltra-compact personal AI workstation featuring the NVIDIA GB10 Grace Blackwell Superchip, equipped with 128 GB of unified LPDDR5X memory, a 20-core Arm CPU, 1 TB, 2 TB, or 4 TB of NVMe storage, and delivering up to 1 PFLOPS of FP4 AI performance in a 150 mm square chassis.\u003c\/p\u003e\u003cp\u003eDesigned to prototype, fine-tune, and run large models locally, and scale to larger workloads by linking two Ascent GX10 systems over the built-in NVIDIA ConnectX-7 SmartNIC at 200 Gb\/s, pre-installed with the NVIDIA AI software stack, CUDA, and support for PyTorch, Jupyter, and Ollama.\u003c\/p\u003e\u003cp\u003eSpace-efficient enough to sit under a monitor at 150 × 150 × 51 mm and 1.48 kg, powered from a standard outlet at under 240 W, with Wi-Fi 7, 10 GbE, four USB-C ports with DisplayPort 2.1, HDMI 2.1, and a Kensington lock, and the ability to link two units for models up to 405 billion parameters.\u003c\/p\u003e\u003cp\u003e$3,999 (1 TB), $4,599 (2 TB), $5,999 (4 TB) at Amazon. Lowest seen: $3,461 (1 TB, March 2026).\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.asus.com\/us\/networking-iot-servers\/desktop-ai-supercomputer\/ultra-small-ai-supercomputers\/asus-ascent-gx10\/\" target=\"_blank\" rel=\"noopener\"\u003eASUS\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"ASUS","offers":[{"title":"1 TB Gen4","offer_id":52457681518808,"sku":"ASUS-GX10-1TB","price":3999.0,"currency_code":"USD","in_stock":true},{"title":"2 TB Gen4","offer_id":52457681551576,"sku":"ASUS-GX10-2TB","price":4599.0,"currency_code":"USD","in_stock":true},{"title":"4 TB Gen5","offer_id":52457681584344,"sku":"ASUS-GX10-4TB","price":5999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/unnamed_d14557a9-97a4-4d38-a2f0-a938416c489f.png?v=1789409994"},{"product_id":"dell-pro-max-18-plus","title":"Dell Pro Max 18 Plus (RTX PRO 5000 24 GB, 128 GB RAM)","description":"\u003cp\u003e18-inch desktop-replacement mobile workstation featuring an Intel Core Ultra 9 285HX processor with vPro, an NVIDIA RTX PRO 5000 Blackwell GPU with 24 GB of GDDR7 at 175 W on a replaceable module, up to 128 GB of DDR5-6400 CAMM2 memory (256 GB per spec), up to 16 TB across four PCIe Gen5 SSDs with RAID, and an 18-inch QHD+ 120 Hz display.\u003c\/p\u003e\u003cp\u003eDesigned for local inference, fine-tuning, and rendering on a certified professional GPU, with Windows 11 Pro, NVIDIA AI Enterprise and CUDA support, Intel OpenVINO across the iGPU and NPU, Thunderbolt 5 for external storage and displays, and Dell's DGFF graphics module that allows the GPU to be serviced or upgraded.\u003c\/p\u003e\u003cp\u003eCarries at roughly 3.5 kg with a bottom hatch for user-serviceable memory and storage, optional 5G WWAN, ISV certifications and enterprise manageability, and the ability to run a 32-billion-parameter model at 4-bit in GPU memory, or far larger models by spilling into system RAM.\u003c\/p\u003e\u003cp\u003e$11,674 as configured (Core Ultra 9 285HX, RTX PRO 5000, 64 GB CAMM2, 2 TB). From $8,284 with the RTX PRO 5000.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.dell.com\/en-us\/shop\/dell-laptops\/dell-pro-max-18-plus-laptop\/spd\/dell-pro-max-mb18250-laptop\/xcto_mb18250_usx\" target=\"_blank\" rel=\"noopener\"\u003eDell\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":52419949494488,"sku":"DELL-MB18250-RTXPRO5000","price":11674.21,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/laptop-dell-pro-max-mb18250nt-bk-ir-fpr-gallery-1.png?v=1789409993"},{"product_id":"rtx-5090-tower-puget-diy","title":"Puget Systems RTX 5090 Tower (32 GB, 192 GB RAM)","description":"\u003cp\u003eFull-size desktop workstation built around a single NVIDIA GeForce RTX 5090 with 32 GB of GDDR7 at 1.79 TB\/s delivering up to 3,352 AI TOPS at FP4, paired with an Intel Core Ultra 9 285K or AMD Ryzen 9 9950X, up to 192 GB of DDR5 memory, PCIe Gen5 NVMe storage, and a 1,000 W or larger power supply.\u003c\/p\u003e\u003cp\u003eDesigned for local inference, fine-tuning, and image or video generation on the fastest single consumer GPU, with Windows 11 or Ubuntu and full CUDA, PyTorch, ComfyUI, and Ollama support, built and burned in by Puget Systems with lifetime labor support.\u003c\/p\u003e\u003cp\u003eFloor-standing on a dedicated 15 A circuit at roughly 800 W under sustained load, no ECC memory or ISV certification, and the ability to run a 32-billion-parameter model at 8-bit or 70 billion at 4-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003eFrom $5,207. Puget Systems configurator; typical build $6,000–7,000. Card supply is the constraint.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.pugetsystems.com\/solutions\/photo-editing-workstations\/generative-ai\/single-gpu-workstation\/\" target=\"_blank\" rel=\"noopener\"\u003ePuget Systems\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Puget Systems","offers":[{"title":"Default Title","offer_id":52419966402776,"sku":"PUGET-RTX5090-TOWER","price":5207.44,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/pic_disp.jpg?v=1789758903"},{"product_id":"lenovo-thinkstation-p3-ultra-rtx-5090","title":"Lenovo ThinkStation P3 Tower Gen 2 (RTX PRO 6000 96 GB)","description":"\u003cp\u003eCompact 23-liter desktop workstation featuring an Intel Core Ultra 9 285 processor with a 36 TOPS integrated NPU, up to 256 GB of DDR5-6400 memory, seven drive bays including four PCIe Gen5 M.2 slots, and a single NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition GPU with 96 GB of GDDR7 ECC memory delivering up to 3,511 TOPS.\u003c\/p\u003e\u003cp\u003eDesigned for local inference and fine-tuning on models that exceed consumer VRAM, with ISV certifications for engineering and media workflows, Windows 11 Pro or Ubuntu, and NVIDIA AI Enterprise support, and expandable to a second single-slot GPU in the remaining PCIe slot.\u003c\/p\u003e\u003cp\u003eSits under a desk on a standard outlet with a 300 W GPU power budget, tool-less chassis access, Kensington lock and chassis intrusion switch, and the ability to run a 70-billion-parameter model at 8-bit or 120 billion at 4-bit entirely in GPU memory.\u003c\/p\u003e\u003cp\u003ePrice available on request. Lenovo configurator pricing; card supply is constrained.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.lenovo.com\/us\/en\/p\/workstations\/thinkstation-p-series\/lenovo-thinkstation-p3-tower-gen-2-intel-workstation\/len102s0019\" target=\"_blank\" rel=\"noopener\"\u003eLenovo\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Lenovo","offers":[{"title":"Request a quote","offer_id":52457681158360,"sku":"LEN-P3T-G2-RTXPRO6000","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/ThinkStation_P3_Tower_Gen_2_CT1_01.png?v=1789409992"},{"product_id":"dell-pro-max-with-gb10","title":"Dell Pro Max GB10 (128 GB, 4 TB)","description":"\u003cp\u003eUltra-compact personal AI workstation featuring the NVIDIA GB10 Grace Blackwell Superchip, equipped with 128 GB of unified LPDDR5X memory, up to 4 TB of NVMe storage, a 20-core Arm CPU, and delivering up to 1 PFLOPS of FP4 AI performance in a 150 mm square chassis.\u003c\/p\u003e\u003cp\u003eDesigned to prototype, fine-tune, and run large models locally, and scale to larger workloads by linking two Dell Pro Max with GB10 systems over the built-in NVIDIA ConnectX-7 SmartNIC at 200 Gb\/s, pre-installed with the NVIDIA AI software stack, CUDA, and support for PyTorch, Jupyter, and Ollama, with Dell Pro Max management and Dell ProSupport behind it.\u003c\/p\u003e\u003cp\u003eSpace-efficient enough to sit under a monitor, powered from a standard outlet at under 240 W, with Wi-Fi 7 and 10 GbE, and the ability to link two units for models up to 405 billion parameters.\u003c\/p\u003e\u003cp\u003e$4,757. Ships from Dell.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.dell.com\/en-us\/shop\/desktop-computers\/dell-pro-max-with-gb10\/spd\/dell-pro-max-fcm1253-micro\" target=\"_blank\" rel=\"noopener\"\u003eDell\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":52419979837656,"sku":"DELL-PROMAX-GB10-4TB","price":4757.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/nvidia-blackwell-products-gb10-update.avif?v=1789174561"},{"product_id":"dgx-station-gb300-any-oem","title":"NVIDIA DGX Station GB300 (784 GB)","description":"\u003cp\u003eDesktop AI supercomputer featuring the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, equipped with 784 GB of coherent memory across 288 GB of HBM3e on the Blackwell Ultra GPU and 496 GB of LPDDR5X on the 72-core Grace CPU, a ConnectX-8 SuperNIC at 800 Gb\/s, and delivering up to 20 PFLOPS of FP4 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train, fine-tune, and serve frontier-scale models on a single desk, with the full NVIDIA AI software stack, DGX OS, CUDA, and NVIDIA AI Enterprise preinstalled, and the ability to link multiple stations over ConnectX-8 for larger workloads or to migrate the same containers unchanged to DGX Cloud.\u003c\/p\u003e\u003cp\u003eTower form factor that sits beside a desk, powered from a standard wall outlet at roughly 1,500 W, built to the same reference design by ASUS, Dell, HP, Lambda, MSI, Supermicro, and others, and able to run models up to 671 billion parameters at FP4 with full context on one unit.\u003c\/p\u003e\u003cp\u003ePrice available on request.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-station\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Request a quote","offer_id":52457680994520,"sku":"NV-DGX-STATION-GB300","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/NVIDIA-DGX-Station-MSI-AI-Supercomputer.webp?v=1789759266"},{"product_id":"supermicro-hgx-8-h200","title":"Supermicro SYS-821GE-TNHR (8× H200, 1,128 GB)","description":"\u003cp\u003eEnterprise 8U rackmount AI server featuring eight NVIDIA H200 SXM5 GPUs on an HGX baseboard, equipped with 1,128 GB of HBM3e memory, dual 5th Gen Intel Xeon Scalable processors, up to 8 TB of DDR5, 19 hot-swap NVMe bays, and delivering up to 32 PFLOPS of FP8 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train and serve frontier-scale models on a single node, with NVLink and NVSwitch connecting all eight GPUs at 900 GB\/s, and scale to multi-node clusters over NVIDIA InfiniBand or 400 GbE, shipping with NVIDIA AI Enterprise support and the standard CUDA, PyTorch, and Triton stack.\u003c\/p\u003e\u003cp\u003eRack-mounted and redundantly powered, with six 3,000 W Titanium supplies, roughly 7 kW of continuous draw, and the ability to run a 405-billion-parameter model at FP8 across all eight GPUs with full context.\u003c\/p\u003e\u003cp\u003ePrice available on request.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.supermicro.com\/en\/products\/system\/gpu\/8u\/sys-821ge-tnhr\" target=\"_blank\" rel=\"noopener\"\u003eSupermicro\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Supermicro","offers":[{"title":"Request a quote","offer_id":52457680928984,"sku":"SMC-SYS-821GE-TNHR","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/SYS-821GE-TNHR_main.webp?v=1789173978"},{"product_id":"asus-esc-n8","title":"ASUS ESC N8-E11V (8× H200, 1,128 GB)","description":"\u003cp\u003e7U rackmount AI server featuring eight NVIDIA H200 SXM5 GPUs on an HGX baseboard, equipped with 1,128 GB of HBM3e memory, dual 5th or 4th Gen Intel Xeon Scalable processors, 32 DDR5 DIMM slots, ten front hot-swap NVMe bays, and delivering up to 32 PFLOPS of FP8 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train and serve frontier-scale models on a single node, with NVLink and NVSwitch connecting all eight GPUs at 900 GB\/s, a one-GPU-to-one-NIC topology across 10+1 PCIe Gen5 slots for 400G InfiniBand or Ethernet scale-out, and full NVIDIA AI Enterprise, CUDA, PyTorch, and Triton support with ASUS Control Center for fleet management.\u003c\/p\u003e\u003cp\u003eAir-cooled with a dedicated two-level airflow path separating CPU and GPU thermals, redundant Titanium power supplies at roughly 7 kW of continuous draw, dual 10 GbE onboard, and the ability to run a 405-billion-parameter model at FP8 across all eight GPUs with full context.\u003c\/p\u003e\u003cp\u003ePrice available on request.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/servers.asus.com\/products\/servers\/gpu-servers\/ESC-N8-E11\" target=\"_blank\" rel=\"noopener\"\u003eASUS\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"ASUS","offers":[{"title":"Request a quote","offer_id":52457680830680,"sku":"ASUS-ESC-N8-E11V","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/download.png?v=1789111240"},{"product_id":"gigabyte-g593","title":"GIGABYTE G593-SD1-AAX3 (8× H200, 1,128 GB)","description":"\u003cp\u003eDense 5U rackmount AI server featuring eight NVIDIA H200 SXM5 GPUs on an HGX baseboard, equipped with 1,128 GB of HBM3e memory, dual 5th or 4th Gen Intel Xeon Scalable processors, 32 DDR5 DIMM slots, eight front hot-swap PCIe Gen5 NVMe bays, and delivering up to 32 PFLOPS of FP8 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train and serve frontier-scale models on a single node, with NVLink and NVSwitch connecting all eight GPUs at 900 GB\/s and dedicated PCIe Gen5 slots for 400G InfiniBand or Ethernet scale-out, shipping with full NVIDIA AI Enterprise, CUDA, PyTorch, and Triton support and GIGABYTE's remote management stack.\u003c\/p\u003e\u003cp\u003eAir-cooled in the smallest 8-GPU footprint on the market at 5U, with six 3,000 W Titanium power supplies in a 4+2 redundant configuration and roughly 7 kW of continuous draw, and the ability to run a 405-billion-parameter model at FP8 across all eight GPUs with full context.\u003c\/p\u003e\u003cp\u003ePrice available on request.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.gigabyte.com\/us\/Enterprise\/GPU-Server\/G593-SD1-AAX3\" target=\"_blank\" rel=\"noopener\"\u003eGIGABYTE\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"GIGABYTE","offers":[{"title":"Request a quote","offer_id":52457680765144,"sku":"GBT-G593-SD1-AAX3","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/Screenshot_2026-09-10_at_8.49.46_PM.png?v=1789098594"},{"product_id":"qct-ai-server","title":"QCT QuantaGrid D75F-9U (8× B200, 1,440 GB)","description":"\u003cp\u003eHyperscale-grade 9U rackmount AI server featuring eight NVIDIA B200 GPUs on an HGX B200 baseboard, equipped with 1.4 TB of HBM3e memory at 64 TB\/s aggregate bandwidth, dual Intel Xeon 6 processors, 32 DDR5 DIMM slots, front hot-swap NVMe bays, and delivering up to 72 PFLOPS of dense FP4 AI performance.\u003c\/p\u003e\u003cp\u003eDesigned to train and serve trillion-parameter-class models on a single node, with fifth-generation NVLink and NVSwitch connecting all eight GPUs at 1.8 TB\/s, one-to-one GPU-to-NIC topology for 400G or 800G InfiniBand and Ethernet scale-out, and full NVIDIA AI Enterprise, CUDA, and Triton support.\u003c\/p\u003e\u003cp\u003eAir-cooled in a standard rack with roughly 10 kW of continuous draw on a three-phase feed, built by the ODM that manufactures much of the world's hyperscale fleet, and able to run a 671-billion-parameter model at FP8 or a 1-trillion-parameter model at FP4 entirely in GPU memory.\u003c\/p\u003e\u003cp\u003ePrice available on request.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.qct.io\/product\/index\/Server\/rackmount-server\/GPGPU-Xeon-Phi\" target=\"_blank\" rel=\"noopener\"\u003eQCT\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"QCT","offers":[{"title":"Request a quote","offer_id":52457680666840,"sku":"QCT-D75F-9U-B200","price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/may-chu-server-supermicro-hgx-b200-8-gpu-as-a126gs-tnbr.webp?v=1789181107"},{"product_id":"lenovo-thinkstation-p3-ultra-sff-gen-2-rtx-pro-2000-blackwell","title":"Lenovo ThinkStation P3 Ultra SFF Gen 2 (RTX PRO 2000 16 GB, 128 GB RAM)","description":"\u003cp\u003eUltra-compact 3.9-liter desktop workstation featuring an Intel Core Ultra 9 285 processor with a 36 TOPS integrated NPU, up to 128 GB of DDR5-6400 memory, multiple PCIe Gen4 and Gen5 M.2 slots, and a single NVIDIA RTX PRO 2000 Blackwell GPU with 16 GB of GDDR7 ECC memory in a 70 W dual-slot envelope.\u003c\/p\u003e\u003cp\u003eDesigned for small-model inference, agent runtimes, and ISV-certified engineering and media workflows at the edge of a desk, with Windows 11 Pro or Ubuntu, NVIDIA AI Enterprise support, optional Thunderbolt 4, and support for up to 12 independent displays across onboard and discrete outputs.\u003c\/p\u003e\u003cp\u003eSmall enough to mount behind a monitor or under a desk, powered from a standard outlet at well under 300 W, with Kensington lock and chassis intrusion support, and the ability to run a 13-billion-parameter model at 4-bit in GPU memory, or larger models slowly by spilling into 128 GB of system RAM.\u003c\/p\u003e\u003cp\u003e$5,720.97 as configured. Lenovo configurator; ~$2,500–3,500 in lighter configurations.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eSpecs \u0026amp; details via \u003ca href=\"https:\/\/www.lenovo.com\/us\/en\/p\/workstations\/thinkstation-p-series\/lenovo-thinkstation-p3-ultra-sff-gen-2-intel\/len102s0022\" target=\"_blank\" rel=\"noopener\"\u003eLenovo\u003c\/a\u003e.\u003c\/em\u003e\u003c\/p\u003e","brand":"Lenovo","offers":[{"title":"Default Title","offer_id":52438467969240,"sku":"LEN-P3ULTRA-G2-RTXPRO2000","price":5720.97,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0839\/7039\/2280\/files\/ThinkStation_P3_Ultra_SFF_Gen_2_CT1_01.png?v=1789409974"}],"url":"https:\/\/shop.hushh.ai\/collections\/nvidia.oembed","provider":"Hussh Shop","version":"1.0","type":"link"}