Nvidia Launches DGX Spark: A Compact AI Powerhouse for Developers

You can now buy Nvidia's personal desktop 'AI supercomputers'

Nvidia will start selling its DGX Spark "personal AI supercomputer" this week. The machine is powerful enough to let users work on sophisticated AI models but small enough to fit on a desktop. From Wednesday, Nvidia said Spark can be ordered online at nvidia.com, as well as from select partners and stores in the US. It has not revealed final pricing but said units would cost $3,000 when it revealed Spark earlier this year. Spark boasts the kind of performance that once required access to pricey, energy-hungry data centers. It could help democratize AI and would be particularly useful for researchers. When first announcing Spark earlier this year (then called Digits), Nvidia CEO Jensen Huang said "placing an AI supercomputer on the desks of every data scientist, AI researcher and student empowers them to engage and shape the age of AI." Buyers can expect to see a variety of similar models on the market as Nvidia has said third-party manufacturers are welcome to make their own versions. Asus, Dell, and HP are among the companies that have said they are working on their own versions of Spark. Spark comes with Nvidia's GB10 Grace Blackwell Superchip, 128GB of unified memory, and up to 4TB of NVMe SSD storage. Nvidia says it can deliver a petaflop of AI performance -- meaning it can do a million billion calculations each second -- and is capable of handling AI models with up to 200 billion parameters. It's also small, comfortably fitting on a desk and running from a standard power outlet. Nvidia calls it "the world's smallest AI supercomputer." We agree: it really is quite tiny. Spark also has a bigger brother, Station, though there's no word on when or if that might hit the general market.

Nvidia's DGX Spark AI mini-PC goes up for sale October 15 -- 1 petaFLOP developer platform was originally slated for May

Potent, pint-size platform also got a $1000 price hike between announcement and launch Nvidia's DGX Spark AI mini-PC got its first star turn at CES this year and was penciled in for a May launch date at the show, but the platform has since experienced delays on its road to market. Whatever wrinkles were preventing a launch have apparently been ironed out. Nvidia has announced that DGX Spark systems will be available to buy starting October 15, both from Nvidia itself and from partners including Dell, Asus, MSI, and HP. As a refresher, the DGX Spark is a Grace Blackwell GB10-powered mini-PC platform that's custom-tailored to the needs of local AI inference and development. Running inference on many of today's state-of-the-art AI models requires far, far more GPU-local memory than even the 32GB that an RTX 5090 can provide. (The RTX Pro 6000 Blackwell offers up to 96GB of GPU-local memory, but that's an $8000+ product before you add in the cost of a host server or workstation). The DGX Spark (formerly known as Project DIGITS) includes a unified, coherent pool of 128GB of LPDDR5X memory that's shared between a 20-Arm-core Nvidia Grace CPU and a Blackwell GPU that purports to deliver up to 1 petaFLOP of AI inferencing performance (assuming a model has been reduced to FP4 quantization with sparsity). The company says a single DGX Spark supports up to 200-billion-parameter models locally (again assuming FP4 quantization). If one Spark isn't enough, two of these units can be connected using the built-in Nvidia ConnectX 7 NIC to double up on memory and compute resources. The DGX Spark runs Nvidia's own DGX OS (a fork of Ubuntu) and supports the all-important CUDA software stack for AI developers. Unlike Strix Halo, which has found a niche as a (costly) gaming chip in devices as diverse as handhelds, the DGX Spark's Arm- and Linux-first nature makes it less appealing as a turn-key gaming platform, though curious enthusiasts can probably get their gaming fix on it with some work. Until now, mini-PCs and laptops built around AMD's Ryzen AI Max+ 395 SoC (aka Strix Halo) have had the market of "relatively reasonably priced chip with a massive memory pool and enough compute for reasonable inferencing performance" all to themselves. Strix Halo supports up to 112GB of GPU memory (out of a possible 128GB of onboard RAM). But those systems don't natively support the widespread CUDA stack, making for some hurdles for developers and enthusiasts who want to get their AI projects up and running. For its part, Nvidia says it's been working with a wide range of software partners to ensure that their tools work well with DGX Spark, including Anaconda, Cadence, ComfyUI, Docker, Google, Hugging Face, JetBrains, LM Studio, Meta, Microsoft, Ollama, and Roboflow, so it seems likely that if you have an LLM you want to run locally, a DGX Spark should be a solid foundation. Nvidia originally said DGX Spark systems would start at $3,000 back in January, but at least the first-party DGX Spark will now retail for $3,999. Even at that price, its tiny size, relatively modest 240W power envelope, and complete turn-key support for the CUDA stack are likely to win it a lot of fans in the burgeoning AI space. We'll have to see whether its long time in the oven has been a liability in a market where everything can still change in the space of hours or days.

Nvidia to Start Selling $3,999 DGX Spark Mini PC This Week

When he's not battling bugs and robots in Helldivers 2, Michael is reporting on AI, satellites, cybersecurity, PCs, and tech policy. Don't miss out on our latest stories. Add PCMag as a preferred source on Google. It's not a consumer desktop, but Nvidia's foray into an AI developer-focused mini PC is finally ready to launch. After starting preorders earlier this year, Nvidia will begin selling the DGX Spark on Wednesday, Oct. 15, through Nvidia.com and select third-party retailers. The 2.6-pound product looks an awful lot like a mini PC, but it's more of an AI training workhorse than a general-purpose computer. Instead of Windows, the product runs Nvidia's DGX OS, the company's custom version of Ubuntu Linux, which has been configured with AI software. At Computex, we saw other vendors, including Asus, Dell, Gigabyte, HP, Lenovo, and MSI, showing off their own takes on the DGX Spark. So, expect to see various implementations using Nvidia's GB10 super chip, like Acer's Veriton GN100 AI Mini Workstation, which arrives in December. Nvidia created the DGX Spark so developers, research scientists, and even students can locally harness the computing power necessary to run cutting-edge AI models. Previously known as Project DIGITS, the product boasts a GB10 super chip, which combines a 20-core Arm-based Grace CPU with a Blackwell GPU that has the same number of CUDA cores as an RTX 5070 graphics card. The Spark has also been outfitted with 128GB of LPDDR5x system memory, shared between the CPU and GPU, along with 4TB of NVMe storage. The product's relatively small package is causing Nvidia to hype it up as "the world's smallest AI supercomputer, delivering data-center-class performance in a compact desktop form factor." Other specs include four USB-C ports, Wi-Fi 7, and an HDMI connector. The mini PC can also be powered from a standard electrical outlet. However, the product isn't cheap. "DGX Spark pricing is $3,999, not including any local taxes or tariffs," Nvidia tells us. While not meant for consumers, the DGX Spark is notable since Nvidia has long been rumored to also be working on a Windows PC that'll feature the company's GPU tech, along with an Arm chip from MediaTek. In addition to the DGX Spark, Nvidia is preparing the larger DGX Station, a full desktop tower that'll boast a more powerful, GB300 Grace Blackwell Ultra chip. Pricing hasn't been announced. But Nvidia plans on selling it later this year with the help of partners including Asus, Boxx, Dell, HP, and Supermicro.

DGX Spark Nvidia's desktop supercomputer: first look

This relatively-affordable AI workstation isn't about going fast; it's about doing everything well enough hands on Nvidia bills its long-anticipated DGX Spark as the "world's smallest AI supercomputer," and, at $3,000 to $4,000 (depending on config and OEM), you might be expecting the Arm-based mini-PC to outperform its less-expensive siblings. But the machine is far from the fastest GPU in Nvidia's lineup. It's not going to beat out an RTX 5090 in large language model (LLM) inference, fine tuning, or even image generation -- never mind gaming. What the DGX Spark, and the slew of GB10-based systems hitting the market tomorrow, can do is run models the 5090 or any other consumer graphics card on the market today simply can't. When it comes to local AI development, all the FLOPS and memory bandwidth in the world won't do you much good if you don't have enough VRAM to get the job done. Anyone who has tried machine learning workloads on consumer graphics will have run into CUDA out of memory errors on more than one occasion. The Spark is equipped with 128 GB of memory, the most of any workstation GPU in Nvidia's portfolio. Nvidia achieves this using LPDDR5x, which, while glacial compared to the GDDR7 used by Nvidia's 50-series, means the little box of TOPS can run inference on models of up to 200 billion parameters or fine tune models of up to 70 billion parameters, both at 4-bit precision, of course. Normally, these kinds of workloads would require multiple high-end GPUs, costing tens of thousands of dollars. By trading a bit of performance and a load of bandwidth for sheer capacity, Nvidia has built a system that may not be the fastest at any one thing, but can run them all. Nvidia isn't the first to build a system like this. Apple and AMD already have machines with loads of LPDDR5x and wide memory buses that have made them incredibly popular among members of the r/locallama subreddit. However, Nvidia is leaning on the fact that the GB10 powering the system is based on the same Blackwell architecture as the rest of its current-gen GPUs. That means it can take advantage of nearly 20 years worth of software development built up around its CUDA runtime. Sure, the ecosystem around Apple's Metal and AMD's ROCm software stacks have matured considerably over the past few years, but, when you're spending $3K-$4K on an AI mini PC, it's nice to know your existing code should work out of the box. Note that the DGX Spark will be available both from Nvidia and in custom versions from OEM partners such as Dell, Lenovo, HP, Asus, and Acer. The Nvidia Founder's Edition we reviewed has a list price of $3,999 and comes with 4TB of storage and gold cladding. Versions from other vendors may have less storage and carry a lower price. The machine itself is styled like a miniaturized DGX-1, measuring just 150 x 150 x 50.5 mm in size and this is no mistake. In 2016, Nvidia CEO and leather jacket aficionado Jensen Huang personally delivered the first DGX-1 to Elon Musk at OpenAI. The system, as it turns out, was the spark that lit the fire behind the generative AI boom. On Monday, Huang visited Musk once again, this time with a DGX Spark in hand. As mini-PCs go, the Spark features a fairly-standard, flow-through design which pulls cool air in the front through a metallic mesh panel and exhausts warm air out the rear. For better or worse, this design choice means that all of the I/O is located on the back of the unit. There, we find four USB-C ports, one of which is dedicated to the machine's 240W power brick, leaving the remaining three available for storage and peripherals. Alongside USB, there's a standard HDMI port for display out, a 10 GbE RJ45 network port, and a pair of QSFP cages which can be used to form a mini-cluster of Sparks connected at 200 Gbps. Nvidia only officially supports two Sparks in a cluster, but we're told there's nothing stopping you from coloring outside the lines and building a miniature supercomputer if you are so inclined. We've certainly seen weirder machines built this way. Remember that Sony Playstation supercluster the Air Force built back in 2010? At the bottom of the system, we find a fairly plain plastic foot which is attached using magnets. Pulling it off doesn't reveal much more than the wireless antennas. It seems that, if you want to swap out the 4 TB SSD for a higher capacity one, you'll need to disassemble the whole thing. Hopefully, partner systems from the likes of Dell, HPE, Asus, and others will make swapping out storage a bit easier. At the Spark's heart is Nvidia's GB10 system on chip (SoC), which, as the name suggests, is essentially a shrunken down version of the Grace Blackwell Superchips found in the company's multi-million dollar rack systems. The chip features two dies: one for the CPU and one for the GPU -- both built on TSMC's 3nm process tech and bonded using the fab's advanced packaging tech. Unlike its bigger siblings, the GB10 doesn't use Arm's Neoverse cores. Instead, the chip was built in collaboration with MediaTek and features 20 Arm cores -- 10 X925 performance cores and 10 Cortex A725 efficiency cores. The GPU, meanwhile, is based on the same Blackwell architecture as we see in the rest of Nvidia's 50-series lineup. The AI arms dealer claims that the graphics processor is capable of delivering a petaFLOP of FP4 compute. Which sounds great, until you consider that there aren't all that many workloads that can take advantage of both sparsity and 4-bit floating point arithmetic. In practice, this means that the most you'll likely see from any GB10 systems is 500 dense teraFLOPS at FP4. Both the graphics processor and CPU are fed by a common pool of LPDDR5x, which, as we've already mentioned, totals 128 GB in capacity and delivers 273 GBps of bandwidth. Out of the box, the Spark can be used in one of two modes: a standalone system with a keyboard, mouse, and monitor, or as a headless companion system accessible over the network from a notebook or desktop. For most of our testing, we opted to use the Spark as a standalone system, as we expect this is how many will choose to interact with the machine. Set up was straightforward. After connecting to Wi-Fi, creating our user account and setting things like the time zone and keyboard layout, we were greeted with a lightly customized version of Ubuntu 24.04 LTS. If you were hoping for Windows, you won't find it here. On the other hand, none of the AI features and capabilities of the system are tied to Copilot or its integrated spyware Recall. That also means that you probably won't be doing much gaming on the thing until Steam decides to release an Arm64 client for Linux. Most of the customizations Nvidia has made to the operating system are under the hood. They include things like drivers, utilities, container plug-ins, Docker, and the all-important CUDA toolkit. Managing these is a headache on the best of days, so it's nice to see that Nvidia took the time to customize the OS to cut down on initial set up time. With that said, the hardware still has a few rough edges. Many apps haven't been optimized for the GB10's unified memory architecture. In our testing, this led to more than a few awkward situations where the GPU robbed enough memory from the system to crash Firefox, or worse, lock up the system. The Spark is aimed at a variety of machine learning, generative AI, and data science workloads. And while these aren't nearly as esoteric as they used to be, they can still be daunting for newcomers to wrap their heads around. A big selling point of the DGX Spark is the software ecosystem behind it. Nvidia has gone out of their way to provide documentation, tutorials, and demos to help users get their feet wet. These guides take the form of short, easy-to-follow playbooks, covering topics ranging from AI code assistants and chatbots to GPU-accelerated data science and video search and summarization. This is tremendously valuable and makes the Spark and GB10 systems feel a lot less like a generic mini PC and more like a Raspberry Pi for the AI era. Whether or not Nvidia's GB10 systems can deliver a level of performance and utility necessary to justify their $3,000+ price tag is another matter entirely. To find out, we ran the Spark through a broad spectrum of fine tuning, image generation, and LLM inference workloads. After days of benchmarks and demos, the best way we can describe the Spark is as the AI equivalent of a pickup truck. There are certainly faster or higher capacity options available, but, for most of the AI work you might want to do, it'll get the job done. The Spark's memory capacity is particularly attractive for fine tuning, a process that involves teaching models new skills by exposing them to new information. A full fine-tune on even a modest LLM like Mistral 7B can require upwards of 100 GB of memory. As a result, most folks looking to customize open models have to rely on techniques like LoRA or QLoRA in order to get the workloads to run on consumer cards. Even then, they're usually limited to fairly small models With Nvidia's GB10, a full fine tune on a model like Mistral 7B is well within reason, while LoRA and QLoRA make fine tuning on models like Llama 3.3 70B possible. Given the limited time available for testing, we opted to fine tune Meta's 3 billion parameter Llama 3.2 model on a million tokens worth of training data. As you can see, with 125 teraFLOPS of dense BF16 performance, the Spark was able to complete the job in just over a minute and half. For comparison, our 48 GB RTX 6000 Ada, a card that just a year ago was selling at roughly twice the price of a GB10 system, managed to complete the benchmark in just under 30 seconds. This isn't too surprising. The RTX 6000 Ada offers nearly 3x the dense BF16 performance. However, it's already pushing the limits of model size and sequence length. Use a bigger model or increase the size of each training sample, and the card's 48 GB of capacity will become a bottleneck long before the Spark starts to struggle. We also attempted to run the benchmark on an RTX 3090 TI, which boasts a peak performance of 160 teraFLOPS of dense BF16. In theory, the card should have completed the test in a little over a minute. Unfortunately, with just 24 GB of GDDR6, it never got the chance, as it quickly triggered a CUDA out of memory error. If you want to learn more about LLM fine tuning, we have a six-page deep dive on the subject that'll get you up and running regardless of whether you've got AMD or Nvidia hardware. Image generation is another memory-hungry workload. Unlike LLMs, which can be compressed to lower precisions, like INT4 or FP4, with negligible quality loss, the same can't be said of diffusion models. The loss in quality from quantization is more noticeable for this class of models, and so the ability to run them at their native FP32 or BF16 precision is a big plus. We tested the DGX Spark by spinning up Black Forest Lab's FLUX.1 Dev at BF16 in the popular ComfyUI web GUI. At this precision, the 12 billion parameter model requires a minimum of 24 GB of VRAM to run on the GPU. That meant the RTX 3090 TI was out once again. Technically, you can offload some of the model to system memory, but doing so can cripple performance, particularly at higher resolutions or batch sizes. Since we're interested in hardware performance, we opted to disable CPU offloading. With ComfyUI set to 50 generation steps, the DGX Spark again wasn't a clear winner, requiring about 97 seconds to produce an image, while the RTX 6000 Ada did it in 37. But, with 128 GB of VRAM, the Spark can do more than simply run the model. Nvidia's documentation provides instructions on fine tuning the diffusion models like FLUX.1 Dev using your own images. The process took about four hours to complete and a little over 90 GB of memory, but, in the end, we were left with a fine tune of the model capable of generating passable images of the DGX Spark, toy Jensen bobble heads, or any combination of the two. For our LLM inference tests, we used three of the most popular model runners for Nvidia hardware: Llama.cpp, vLLM, and TensorRT LLM. All of our inference tests were run using 4-bit quantization, a process that compresses model weights to roughly a quarter of their original size, while quadrupling their throughput in the process. For Llama.cpp, we used the Q4_K_M quant. For vLLM and TensorRT LLM, we opted for NVFP4 or MXFP4 in the case of gpt-oss. Most users running LLMs on the Spark aren't going to have multiple API requests hitting the system simultaneously, so we started by measuring batch-1 inference performance. On the left, we measured the token generation rate for each of the models tested. On the right, we recorded the time to first token (TTFT), which measures the prompt processing time. Of the model runners, Llama.cpp achieved the highest token generation performance, matching, even beating out vLLM and TensorRT LLM in nearly ever scenario. When it comes to prompt processing, TensorRT achieved performance significantly better than either vLLM or Llama.cpp. We'll note that we did see some strange behavior with certain models, some of which can be attributed to software immaturity. vLLM, for instance, launched using weights-only quantization, which meant it couldn't take advantage of the FP4 acceleration in the GB10's tensor cores. We suspect this is why the TTFT in vLLM was so poor compared to TensorRT. As the software support for the GB10 improves, we fully expect this gap to close considerably. The above tests were completed using a relatively short input and output sequence like you might see in a multi-turn chat. However, this is really more of a best-case scenario. As the conversation continues, the input grows, putting more pressure on the compute-heavy prefill stage, making a longer wait for the model to start responding. To see how the Spark performed as the context grows, we measured its TTFT (X-axis) and token generation (Y-axis) for gpt-oss-120B at various input sizes ranging from 4096 tokens to 65,536. For this test, we opted to use TensorRT, as it achieved the highest performance in our batch testing. As the input length increases, the generation throughput decreases, and the time to first token climbs, exceeding 200 milliseconds by the time it reaches 65,536 tokens. That's equivalent to roughly 200 double-spaced pages of text. This is incredibly impressive for such a small system and showcases the performance advantage of native FP4 acceleration introduced on the Blackwell architecture. For models that can fit within the GPUs' VRAM, their higher memory bandwidth gives them an edge in token generation performance. That means a chip with 960 GBps of memory bandwidth is going to be faster than a Spark at generating tokens. But that's only true so long as the model and context fit in memory. This becomes abundantly clear as we look at the performance delta between our RTX 6000 Ada, RTX 3090 TI, and the Spark. As models push past 70 billion parameters, memory bandwidth becomes irrelevant on all but the most expensive workstation cards, simply because they don't have the memory capacity necessary to run them anymore. And sure, both the 3090 TI and 6000 Ada can fit medium-size models like Qwen3 32B or Llama 3.3 70B at 4-bit precision, but there's not much room leftover for context. The key value cache that keeps track of something like a chat, can consume tens or even hundreds of gigabytes depending on how big the context window is. Another common scenario for LLMs is using them to extract information from large quantities of documents. In this case, rather than processing them sequentially one at a time, it's often faster to process them in larger batches of four, eight, 16, 32, or more. To test the Spark's performance in a batch processing scenario, we tasked it with using gpt-oss-120B to process a 1,024 token input and generate a 1,024 token response at batch sizes ranging from one to 64. On the X-axis, we've plotted the time in seconds required to complete the batch job. On the Y-axis, meanwhile, we've plotted overall generative throughput at each batch size. In this case, we see performance plateaus at around batch 32, as it takes longer for each subsequent batch size to complete. This indicates that, at least for gpt-oss-120B, the Spark's compute or memory resources are becoming saturated around this point. While the Spark is clearly intended for individual use, we can easily see a small team deploying one or more of these as an inference server for processing data or documents locally. Similar to the multi-batch benchmark, we're measuring performance metrics like TTFT, request rate, and individual performance at various levels of concurrency. With four concurrent users, the Spark was able to process one request every three seconds while maintaining a relatively interactive experience at 17 tok/s per user. As you can see, the number of requests the machine can handle increases with concurrency. Up to 64 concurrent requests, the machine was able to maintain an acceptable TTFT of under 700 ms, but that comes at the consequence of a plodding user experience as the generation rate plummets to 4 tok/s. This tells us, in this particular workload, the Spark has plenty of compute necessary to keep up with a large number of concurrent requests, but is bottlenecked by a lack of memory bandwidth. With that said, even a request rate of 0.3 per second is a lot more than you think, working out to 1,080 requests an hour -- enough to support a handful of users throughout the day with minimal slow downs. As we alluded to earlier, the DGX Sparks' real competition isn't consumer or even workstation GPUs. Instead, platforms like Apple's M4 Mac Mini and Studio or AMD's Ryzen Al Max+ 395 based systems, which you may recognize by the name Strix Halo, pose the biggest challenge. These systems feature a similar unified memory architecture and a large quantity of fast DRAM. Unfortunately, we don't have any of these systems on hand to compare against just yet, so we can only point to speeds and feeds. Even then, we don't have complete information. Putting the DGX Spark in that context, the $3,000-$4,000 price tag for a GB10-based system doesn't sound quite so crazy. AMD and its partners are seriously undercutting Nvidia on price, but the Spark is, at least on paper, much faster. A Mac Studio with an equivalent amount of storage on the other hand is a fair bit more expensive but boasts higher memory bandwidth, which is going to translate into better token-generation. What's more, if you've got cash to burn on a local token factory, the machine can be specced with up to 512 GB on the M3 Ultra variant. The Spark's biggest competition could however come from within. As it turns out, Nvidia actually makes an even more powerful Blackwell-based mini PC that, depending on your config, may even be cheaper. Nvidia's Jetson Thor developer kit is primarily designed as a robotics development platform. With twice the sparse FP4, 128 GB of memory, and 273 GBps of bandwidth, the system offers a better bang for your buck at $3,499 than the DGX Spark. Thor does have less I/O bandwidth with a single 100 Gbps QSFP slot that can be broken out into four 25 Gbps ports. As cool as the Spark's integrated ConnectX-7 NICs might be -- we haven't had a chance to test them just yet -- we expect many folks considering one would have happily forgone the high-speed networking in favor of a lower MSRP. Whether or not the DGX Spark is right for you is going to depend on a couple of factors. If you want a small, low-power AI development platform that can pull double duty as a productivity, content creation, or gaming system, then the DGX Spark probably isn't for you. You're better off investing in something like AMD's Strix Halo or a Mac Studio, or waiting a few months until Nvidia's GB10 Superchip inevitably shows up in a Windows box. But, if your main focus is on machine learning, and you're on the market for a relatively affordable AI workstation, there are a few options that tick as many boxes as the Spark. ®

NVIDIA starts selling its $3,999 DGX Spark AI developer PC

NVIDIA's DGX Spark AI computer revealed earlier this year goes on sale today for $3,999, the company announced. Though relatively tiny, it hosts the the company's entire AI platform including GPUs and CPUs, along with NVIDIA's AI software stack "into a system small enough for a lab or an office," NVIDIA said. The Spark isn't something you'd buy to play Baldur's Gate 3, though. It's designed to give developers, researchers and data scientists enough computer power to run complex AI models. Early recipients of the PCs include Anaconda, Google, Hugging Face, Meta and Microsoft. NVIDIA CEO Jensen Huang even hand-delivered a unit to Elon Musk at SpaceX's headquarter in Starbase, Texas. The DGX has plenty of power on offer despite its diminutive 2.6 pound size. It boasts NVIDIA's GB10 super system-on-chip that weds a 20-core ARM CPU with a Blackwell GPU powered by the same number of cores as an RTX 5070 GPU. It's outfitted with 128GB of LPDDR5x RAM shared between the CPU and GPU and includes 4TB of NVMe storage, along with four USB-C ports, Wi-Fi 7 and an HDMI connector. NVDIA calls it "the world's smallest AI supercomputer." The DGX Spark runs Nvidia's DGX OS, a custom version of Ubuntu Linux that's configured with AI software. With that, developers can access NVIDIA AI models, libraries and microservices in order to do chores like refining image generation or creating AI chatbots. The DGX Spark is also an entry point for similar machines. Other vendors like Dell, HP, Lenovo and ASUS showed off similar AI-oriented mini PC's at Computex this year using the same GB10 chip, with Acer's Veriton GN100 being one example. The DGX Spark mini PC is now on sale for $3,999 through NVIDIA and its partners. While not cheap, it's a drop in the bucket for AI developers and all of the companies listed above, and considering the hardware inside, the price doesn't seem unreasonable. NVIDIA is also working on the DGX Station that will feature GB300 Grace Blackwell Ultra Desktop Superchip, with 20 petaflops of performance and 784GB of unified system memory. A price has yet to be announced for that model

Jensen Huang hand-delivers Nvidia DGX Spark desktop AI supercomputer to Elon Musk ahead of launch

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. In a nutshell: Nvidia's DGX Spark, which the company calls a desktop AI supercomputer, launches tomorrow. One of the first units has been hand-delivered by Jensen Huang to an important customer: Elon Musk. Team Green's boss met the world's richest person at SpaceX's facility in Texas for the handover. After being teased at CES 2025, when it was still called Project Digits, Nvidia officially revealed the DGX Spark during its GTC conference in March. Pre-orders for the DGX Spark opened earlier this year, and the desktop goes on sale tomorrow, October 15, via Nvidia's website and retailers such as Micro Center. There will be different versions of the machine from the likes of Asus, Dell, Gigabyte, HP, Lenovo, and MSI. Nvidia might be the world's largest company by market cap ($4.5 trillion), but the firm still likes to give its product launches plenty of promotion. For the DGX Spark, Huang traveled to Starbase, the city in Texas that acts as SpaceX's testing and production location, to meet fellow CEO Elon Musk. Nvidia's press release notes that Huang recounted the story of delivering the first DGX system to OpenAI and explained how Spark takes that mission further. Given the animosity between Musk and OpenAI, one has to wonder how much he appreciated this tale. "Imagine delivering the smallest supercomputer next to the biggest rocket," Huang said. It's noted that the handoff came as SpaceX prepared for the 11th test of Starship, the world's most powerful launch vehicle. Although it is a desktop, the DGX Spark is primarily aimed at developers, researchers, and creators. Nvidia says it is designed to bring data center-level AI capabilities to a desktop or lab environment. The PC has the kind of impressive specs one would expect in something advertised as a supercomputer: a GB10 Grace Blackwell Superchip that combines a Blackwell GPU and a 20-core Arm-based Grace CPU, 128GB of unified memory (accessible by both CPU and GPU), 4TB of NVMe storage, Nvidia ConnectX networking for clustering, and Nvidia NVLink-C2C for 5x PCIe bandwidth. The 2.6-pound Spark comes with features found on typical home desktops, too, including four USB-C ports, Wi-Fi 7, and an HDMI connector. It can also be powered from a standard electrical outlet. Purchasing one petaflop of AI performance at FP4 precision and 200 Gbps of high-speed networking doesn't come cheap. The Spark costs $3,999, not including any local tariffs or taxes, Nvidia explained. It's still cheaper than the upcoming DGX Station, a full desktop tower packing a more powerful GB300 Grace Blackwell Ultra. No word on its price, but an Asus desktop (ExpertCenter Pro ET900N G3) featuring the same chip and offering 20 petaflops of AI performance is expected to be priced at over $30,000.

Nvidia's miniaturized Grace-Blackwell workstations are here

Nvidia's tiniest Grace-Blackwell workstation is finally making its way to store shelves this week, the better part of a year after the GPU giant first teased the AI mini PC, then called Project Digits, at CES. Since rebranded as the DGX Spark, the roughly NUC-sized system pairs a Blackwell GPU capable of delivering up to a petaFLOP of sparse FP4 performance with 128 GB of unified system memory and 200 Gbps of high-speed networking. But with a price starting around $3,000, small doesn't mean cheap. Then again, it's not exactly aimed at mainstream PC buyers. The systems, which will also be available under various brand names from OEM partners, won't even come with Windows. A Copilot+ PC this is not. Instead, it ships with a custom spin of Ubuntu Linux. Spark is actually intended for AI and robotics developers, data scientists, and machine learning researchers looking for a lower-cost workstation platform that's still capable of running models up to 200 billion parameters in size. These kinds of workloads are incredibly memory-hungry, which makes running them on consumer graphics processors impractical. High-end workstation cards, like the RTX Pro 6000, can be had with up to 96 GB of speedy GDDR7, but a single card will set you back more than $8,000, and that's before you factor in the rest of the platform cost. At the time of launch, the DGX Spark is technically Nvidia's highest capacity workstation GPU -- at least until its Blackwell Ultra-based DGX Station makes its debut. Powering the DGX Spark is the GB10 system-on-a-chip, which is essentially a miniaturized version of the Grace-Blackwell Superchips that power its flagship NVL72 rack systems. As we explored back at Hot Chips, the GB10 is composed of two compute dies connected at 600 GB/s via Nvidia's proprietary NVLink chip-to-chip interconnect tech. And, in case you're wondering, this same technology will eventually be used to mesh Nvidia's GPUs to Intel's future client CPUs as part of a tie-up between the two chip heavyweights. The GPU tile is capable of delivering up to a petaFLOP of sparse FP4 or around 31 teraFLOPS at single precision (FP32) -- putting it on par with an RTX 5070 in terms of raw performance. Yes, the $550 consumer card does offer more than twice the memory bandwidth, but with just 12 GB of GDDR7, you'll be fairly limited in terms of what models and AI workloads you can run. Unlike Nvidia's original Grace CPU, the GB10's CPU tile isn't using Arm's Neoverse V2 cores. Instead, the chip was designed in collaboration with MediaTek and features 20 ARMv9.2 cores. Ten of those are Arm's high-performance X925 cores, while the remaining are based on its efficiency-optimized Cortex A725 cores. Much like Apple's M-series and AMD's Strix Halo SoCs, both the GB10's CPU and GPU are fed by a common pool of LPDDR5x. This tight coupling of compute and memory has allowed these chipmakers to achieve bandwidths more than twice that of conventional PC platforms today. In the case of the GB10, Nvidia is claiming 273 GB/s of memory bandwidth. One thing you'll find on the Spark that you won't find on other systems is high-speed networking. Just like Nvidia's datacenter platforms, the Spark's GB10 is accompanied by an integrated ConnectX-7 networking card with a pair of QSFP Ethernet ports out the back. While you could theoretically use these for high-speed networking, the ports are actually designed to connect two DGX Sparks together, effectively doubling its fine-tuning and inferencing capabilities. In this config, Nvidia says users will be able to run inference on models up to 405 billion parameters at 4-bit precision. DGX Spark systems from Nvidia, Acer, Asus, Dell Tech, Gigabyte, HPE, Lenovo, and MSI will be available for purchase starting Oct. 15. ®

DGX Spark: NVIDIA unveils its smallest AI computer at $3,999

Although it looks like a compact desktop, the 2.6-pound DGX Spark isn't designed for everyday consumers. It's an AI powerhouse built to help developers, research scientists, and students run advanced models locally. NVIDIA describes it as "the world's smallest AI supercomputer," promising data-center-class performance in a form factor that fits on a desk. At the heart of the DGX Spark is NVIDIA's new GB10 Grace Blackwell Superchip. The processor combines a 20-core Arm-based Grace CPU with a Blackwell GPU carrying the same CUDA cores as the RTX 5070 graphics card. \ NVIDIA has optimized this setup for desktop AI development, allowing users to fine-tune and run large models without relying on remote cloud access. The GB10 delivers up to 1,000 trillion operations per second of AI compute, thanks to fifth-generation Tensor Cores and FP4 support. The system also features NVLink-C2C interconnect technology, which offers five times the bandwidth of PCIe Gen 5. This allows seamless data movement between the CPU and GPU, making it ideal for memory-heavy workloads such as model inference, robotics simulation, and generative AI tasks.

Nvidia shrunk a data center into a desktop, and it's calling it the DGX Spark

Smaller than a Mac Mini, but built to train giant AI models, the DGX Spark brings data-center muscle to your desktop for a premium price tag. What's happened? Nvidia's DGX Spark is here and is being billed as the world's smallest AI supercomputer. With the GB10 Grace Blackwell Superchip inside, it delivers up to 1 petaFLOP of compute, 128 GB unified memory, and the ability to run models with up to 200 billion parameters -- all for $3,999. It's not your typical desktop PC, though. Instead, it's a data-center engine in a compact shell. It's already drawing attention for how it blurs the line between workstation and data-center system. Powered by the GB10 Grace Blackwell Superchip, packing GPU + CPU with unified memory and NVLink-C2C interconnect. Delivers up to 1 petaFLOP of AI compute (FP4 precision) and can support models up to 200 billion parameters. Comes with 128 GB unified memory and up to 4 TB NVMe SSD for high-speed data storage. Pricing landed at $3,999, up from earlier expectations of $3,000. Compact form factor with ports including USB4, 10 GbE LAN, and support for ConnectX-7 networking for clustering two DGX Sparks into a 405 billion-parameter system. Designed with AI developers in mind, it supports major frameworks like PyTorch and TensorFlow, along with NVIDIA's full AI stack. Why this is important: This marks one of the clearest steps yet in bringing real AI compute to desktops, stripping reliance on remote clusters. By shrinking enterprise-grade power into something that fits under a monitor, Nvidia is breaking down the wall between research labs and living rooms. It's a move that could redefine how and where AI innovation happens. The DGX Spark also serves as a statement of intent from Nvidia: AI is no longer just a cloud service, it's a local tool for creators, researchers, and developers. Shifts AI development workflows from cloud-only to hybrid/local setups. It lets smaller teams, researchers, and startups prototype and fine-tune large models in-house. Makes serious AI horsepower more affordable, considering $3,999 is pocket change next to data-center costs. Serves as a signal that heavy AI computing doesn't have to stay locked in server farms. Forces rivals to rethink how much AI muscle can be squeezed into compact, power-efficient machines. Recommended Videos Why should I care? For most people, this won't mean much as the DGX Spark isn't here to replace a Mac Mini or become your next home PC. But that's exactly the point. This isn't a consumer desktop; it's a miniature supercomputer built for developers, researchers, and startups working on large-scale AI models. If you're deep in machine learning, running training jobs, or experimenting with generative AI, the DGX Spark could be a game-changer. It brings serioṭs data-center muscle to your desk, letting you run massive workloads locally without renting cloud GPUs. Think of it as a personal AI lab: compact, powerful, and unapologetically overkill for anyone not doing high-end AI work. Researchers and AI hobbyists could train or fine-tune larger models locally, reducing latency and cloud costs. Sensitive or proprietary datasets can stay on-premises, avoiding cloud exposure. With the ability to cluster two units, you can push into even bigger model territory (405B parameter class). It acts as a bridge: build on Spark locally, then deploy to Nvidia's DGX Cloud or larger AI infrastructure. Okay, so what's next? Well, Nvidia isn't stopping with the DGX Spark. The company has already confirmed that major PC makers, including Acer, Dell, HP, Lenovo, and MSI, are lining up their own versions. As such, you can expect to see Spark-inspired systems popping up everywhere once production ramps up. On Nvidia's end, the focus now shifts to building out its DGX software ecosystem, so developers can easily scale their workloads from desktop to cloud without skipping a beat. It's part of a bigger trend we're seeing with AI compute going personal. What used to take server racks and enterprise budgets is slowly being squeezed into smaller, quieter boxes.

'Imagine delivering the smallest supercomputer next to the biggest rocket': Jensen delivers a DGX Spark to Musk at SpaceX facility but some think the Nvidia launch is little more than a 'PR stunt'

Is this mass production or a limited launch for the mini 'supercomputer'? Nvidia has finally started shipping its first DGX Spark mini 'supercomputers' and Musk's got his mitts on the first one. The company relays, "Nvidia founder and CEO Jensen Huang arrived at the SpaceX facility -- amid towering engines and gleaming steel -- to hand-deliver the company's just-launched DGX Spark to Elon Musk." Huang joked, "Imagine delivering the smallest supercomputer next to the biggest rocket," referring to Starship, an in-development orbital rocket with the biggest capacity of any so far. Nvidia's DGX Spark was first known as Project Digits and is a small home-user AI supercomputer built with Grace Blackwell silicon. More specifically, that silicon is GB10, a chip with a one petaFLOP Blackwell GPU and a Grace CPU with 20 Arm cores, plus 128 GB of LPDDR5X unified memory and up to 4 TB of NVMe storage. Part of the reason we've had our eye on GB10 and DGX Spark as PC gamers is that there's been lots of talk about it perhaps being transplanted into an N1 APU to make for Windows on Arm laptop chips. That's recently been confirmed by Huang, too, who said Nvidia has "a new Arm product that's called N1", which goes into DGX Spark. That all-Nvidia N1 laptop chip has a bit of an air of myth about it, though, given its seemingly never-ending delays. These delays seem to have made at least some in the industry a little sceptical about GB10 in general. Charlie Demerjian of SemiAccurate, for instance, reckons this GB10/N1X launch is a 'PR stunt' because despite the chip "now looking like it will be 18 months late", Nvidia's strategy, he thinks, is to "seed a few units to the media and claim it is production." Apparently, "SemiAccurate has heard multiple claims of volumes for this seeding program but none exceed two digits. That is a total for all manufacturers, not per OEM." And several "were given a pretty stringent list of do's and don'ts for their 'independent' testing," which Demerjian sees to mean that "Nvidia is seeding a scant few units that don't actually work right to select media" to "make things look like it is production, or close to." I can't confirm or deny any of this, but whether it's a "PR stunt" or otherwise, we can at least see that DGX Spark exists in the wild and is in the hands of at least some researchers and so on. On Nvidia's side, the company says these "early recipients" are "testing, validating and optimizing their tools, software and models for DGX Spark." Also, according to Nvidia, "Acer, ASUS, Dell Technologies, GIGABYTE, HP, Lenovo and MSI are rolling out systems that put petaflop AI on your desk, transforming the desktop into an AI launchpad." No word on those N1 laptops with Windows on Arm, though. Previous rumours -- veritably ancient rumours, now, really -- had it that we'd see them featuring in some Alienware gaming laptops with RTX 4070-level performance. Call me a dreamer, but I'm still holding out for that one. Don't burst my bubble, okay?

Jensen Huang Delivers Elon Musk First Units of 'World's Smallest AI Supercomputer' | AIM

NVIDIA announced on October 13 that it will begin shipping DGX Spark. It is touted as the world's smallest AI supercomputer. It is priced at $3,999. Jensen Huang, CEO of NVIDIA, hand-delivered the first units of DGX Spark to Elon Musk, who leads SpaceX, Tesla, and X. "The exchange was a connection to the supercomputer's origins, as Musk was among the team that received the first NVIDIA DGX-1 supercomputer from Huang in 2016," said the company. Announced earlier this year, DGX Spark is primarily designed to handle a wide range of AI workloads in a compact form factor. It is equipped with the whole of NVIDIA's AI stack -- from GPUs, CPUs, networking, CUDA libraries, and more. The company stated it provides a petaflop of AI performance, equipped with 128 GB of unified memory. Powered by the GB10 Blackwell Superchip, it can perform inferences on AI models with up to 200 billion parameters and fine-tune models containing up to 70 billion parameters. "In addition, DGX Spark lets developers create AI agents and run advanced software stacks locally," said NVIDIA. Besides Musk, companies such as Microsoft, Google, Hugging Face, Meta, and others have received the supercomputer for testing and optimising their tools and offerings. "DGX Spark allows us to access peta-scale computing on our desktop," said Kyunghyun Cho, professor of computer and data science at the NYU Global Frontier Lab. "This new way to conduct AI research and development enables us to rapidly prototype and experiment with advanced AI algorithms and models -- even for privacy- and security-sensitive applications, such as healthcare."

NVIDIA Unveils DGX Spark for AI Developers Worldwide

NVIDIA has announced the launch and global availability of the DGX Spark, a compact AI supercomputer designed to bring data center-class performance to desktop developers. The system delivers up to 1 petaflop of AI performance and integrates 128 GB of unified CPU-GPU memory. It enables local training and inference for models with up to 200 billion parameters and fine-tuning of models up to 70 billion parameters, reducing dependency on cloud infrastructure. Powered by the NVIDIA GB10 Grace Blackwell Superchip, DGX Spark combines ConnectX-7 200 Gb/s networking and NVLink-C2C interconnects, which offer five times the bandwidth of PCIe Gen 5. The platform comes pre-installed with NVIDIA's full AI software stack, including CUDA, AI Enterprise, and NIM microservices, allowing developers to deploy, test, and customize models such as FLUX.1, Cosmos Reason VLM, or Qwen3 directly on-device. DGX Spark's introduction aligns with NVIDIA's long-term mission to democratize AI compute. Jensen Huang personally delivered the first unit to Elon Musk at SpaceX's Starbase in Texas, symbolically linking back to the original DGX-1 delivered to OpenAI in 2016. Early adopters include Google, Microsoft, Meta, Anaconda, Hugging Face, and JetBrains, who are optimizing their workflows for Spark's architecture. Global research institutions, including the NYU Global Frontier Lab, are leveraging the system for secure, high-performance local experimentation in sensitive fields such as healthcare. DGX Spark will be available for order on October 15 via NVIDIA.com and through partners including Acer, ASUS, Dell Technologies, GIGABYTE, HP, Lenovo, MSI, and Micro Center.

Nvidia starts shipping its smallest AI supercomputer: DGX Spark

Nvidia has started shipping the DGX Spark, its smallest desktop AI supercomputer. The system integrates the company's Grace Blackwell architecture to support advanced artificial intelligence workloads locally and is being released through major hardware partners. The DGX Spark combines GPUs, CPUs, networking, and specialized AI software into a single unit. Nvidia states the system can deliver up to one petaflop of AI performance and includes 128 GB of unified memory. It arrives with preinstalled software designed for both AI model training and inference tasks. Orders for the base unit, priced at $4,000, will open on October 15, 2025, through Nvidia's website. Systems from partners, including Acer, ASUS, Dell Technologies, GIGABYTE, HP, Lenovo, and MSI, are available globally. Despite its performance claims, the DGX Spark's 273 GB/s memory bandwidth limits its throughput for production-level inference, positioning it more for prototyping and experimental work. Benchmarks indicate its performance is approximately four times slower than the RTX Pro 6000 Blackwell workstation GPU. Due to its bandwidth constraints, it also trails the performance of the RTX 5090 when running large models. The unit's compact design maintains stable thermals while under load. It draws around 170 W of power from an external USB-C source, a configuration that can present challenges for office deployments. The total cost of ownership becomes less direct when scaling the system. For instance, connecting two DGX Spark units to run a 405-billion-parameter model requires additional ConnectX-7 200 GbE hardware, which is not included in the base price and complicates cost comparisons with public-cloud GPU options. The DGX Spark is identified as suitable for specific, controlled environments. The NYU Global Frontier Lab noted its applicability for privacy-sensitive work in healthcare, which creates a basis for managed services covering procurement, HIPAA-compliant rollouts, and ongoing security. The system's ability to support fine-tuning of models up to 70 billion parameters appeals to educational institutions and smaller biotech firms seeking local customization without exposing data to the cloud. This has opened a niche for turnkey AI lab setups. Nvidia's extensive partner network, spanning from Dell and HP to Lenovo and ASUS, provides a broad channel for market distribution. This allows integrators to bundle services such as installation, training, and support for organizations that do not have in-house AI expertise. In another recent development, Nvidia's CEO highlighted the company's first direct partnership with OpenAI.

NVIDIA unveils world's smallest AI supercomputer release date

TL;DR: NVIDIA will begin shipping the DGX Spark, the world's smallest AI supercomputer, on October 15 starting at $3,999. Powered by Grace Blackwell architecture and NVIDIA GPUs, it delivers 1 petaflop performance and 128GB unified memory, enabling developers to run large AI models locally with preinstalled AI software. NVIDIA has unveiled when it will begin shipping the world's smallest AI supercomputer, with the company taking to social media to showcase NVIDIA CEO Jensen Huang hand-delivering one of the first devices to SpaceX CEO Elon Musk. The NVIDIA DGX Spark is a new class of computer that is aimed at researchers, engineers, teams of scientists, and even consumers who are interested in running custom AI models. The DGX Spark is built on NVIDIA Grace Blackwell architecture and integrates NVIDIA GPUs, ARM CPUs, networking, CUDA libraries, and NVIDIA AI software, creating a device capable of running 200 billion parameter AI models. The DGX Spark delivers a petaFLOP of AI performance and 128GB of unified memory, enabling developers to run 70 billion parameters locally - all within a footprint that's about the size of your outstretched hand. More specifically, the DGX Spark's 1 petaFLOP of performance is accelerated by a NVIDIA GH10 Grace Blackwell Superchip, NVIDIA ConnectX-7 200 Gb/s networking, and NVIDIA NVLink-C2C technology, which enables 5x the bandwidth of fifth-generation PCIe and 128GB of CPU-GPU coherent memory. Developers should note that the DGX Spark comes preinstalled with the NVIDIA AI software stack, allowing developers to begin working on projects right out of the box. NVIDIA's CEO Jensen Huang hand-delivered one of the first DGX Sparks to SpaceX CEO Elon Musk to pay homage to the legacy of supercomputers' origins, as Musk received the first NVIDIA DGX-1 supercomputer in 2016. "In 2016, we built DGX-1 to give AI researchers their own supercomputer. I hand-delivered the first system to Elon at a small startup called OpenAI - and from it came ChatGPT, kickstarting the AI revolution. DGX-1 launched the era of AI supercomputers and unlocked the scaling laws that drive modern AI. With DGX Spark, we return to that mission - placing an AI computer in the hands of every developer to ignite the next wave of breakthroughs," said Jensen Huang, founder and CEO of NVIDIA NVIDIA writes in its press release that the DGX Spark will begin shipping on Wednesday, October 15, and will start at $3,999. "DGX Spark allows us to access peta-scale computing on our desktop. This new way to conduct AI research and development enables us to rapidly prototype and experiment with advanced AI algorithms and models - even for privacy- and security-sensitive applications, such as healthcare," said Kyunghyun Cho, professor of computer and data science at the NYU Global Frontier Lab

Nvidia's Smallest AI Supercomputer Goes On Sale With These Features

The system is equipped with the GB10 Grace Blackwell Superchip SoC Nvidia is finally taking its latest artificial intelligence (AI) supercomputer to the market. The company announced on Monday that it will start shipping the Nvidia DGX Spark, claimed to be the world's smallest AI supercomputer, starting October 15. Despite its small form factor, the chipmaker claims that it is powerful enough to let users work on sophisticated AI models. Successor to the DGX-1 that launched in 2016, it is equipped with 128GB of unified memory and offers one petaflop of AI performance. Nvidia DGX Spark Price and Availability The Nvidia DGX Spark price is set at $3,999 (roughly Rs. 3.5 lakh). The AI supercomputer can be ordered starting October 15 on the company's website. Additionally, its PC partners, such as Acer, Asus, Dell, Gigabyte, HP, Lenovo, and MSI, will also release their customised versions, which individuals can opt for. For instance, the Acer Veriton GN100 will also be available in the market soon. Nvidia DGX Spark Specifications Nvidia DGX Spark is equipped with the company's GB10 Grace Blackwell Superchip chipset and delivers up to one petaflop (1,000 trillion floating-point operations per second) of AI performance. The chipset allows for on-device complex computing as well as the development of processing-intensive applications. However, the company has designed the supercomputer for AI workflows. The chipset features an Nvidia Blackwell GPU with Cuda and Tensor cores and an Arm-based Nvidia Grace CPU with 20 efficiency cores. The chipset was designed in collaboration with MediaTek. Notably, the chipset is paired with 128GB of unified memory and up to 4TB of NVMe SSD storage. It can handle AI models with up to 200 billion parameters and fine-tune models of up to 70 billion parameters. The company says it can also create AI agents. Coming to the dimensions, the Nvidia DGX Spark measures 150 x 150 x 50.5mm and weighs 1.2kg. It requires a connection with a 240W power outlet to work. The company CEO, Jensen Huang, delivered one of the first units of the supercomputer to Elon Musk at SpaceX on Monday. Other early recipients include Google, Hugging Face, JetBrains, Meta, Microsoft, Ollama, and others.

NVIDIA's Jensen Huang Hand-Delivers the "World's Smallest Supercomputer," the DGX Spark, to Elon Musk, Right Alongside Its Retail Launch

NVIDIA's CEO has delivered one of the firm's most optimistic products, the DGX Spark, to billionaire Elon Musk, highlighting the importance of the device in Jensen's eyes. The DGX Spark is known to be one of the most compact devices available, offering computational performance that no one could have ever thought possible. The product aligns with Jensen's core idea of making AI accessible to everyone. The DGX Spark was showcased at CES 2025, and now, NVIDIA's CEO has managed to deliver a unit of the mini-supercomputer to Elon Musk, right around the 11th test of Starship. This move also took us on a trip down memory lane, to a time when NVIDIA's CEO delivered one of the first units of the DGX-1 to Musk during his tenure with OpenAI. Huang arrived walking past rows of engineers who waved and grinned. Moments later, Musk appeared in the cafeteria, greeting staff and opening donuts and chips for kids before grabbing a slice of pizza. Huang joined him, recounting the story of delivering the first DGX system to OpenAI and explaining how Spark takes that mission further. - NVIDIA NVIDIA's DGX Spark is now expected to be available through retailers by October 15th. It can be ordered directly from the firm's official website, along with vendor availability from Acer, ASUS, Dell Technologies, GIGABYTE, HP, Lenovo, and MSI. We have deep-dived into relevant SKUs and the specifics of each custom model in a previous post here, but for a quick rundown of the DGX Spark specifications, here are the main highlights: The mini-supercomputer experienced a slight delay in its launch, as retail availability was previously expected in July. However, given the complexities involved with the custom NVIDIA and MediaTek GB10 SoC, the launch was pushed ahead to October. The device offers immense performance for professionals, bringing massive AI computational power in the palm of your hands, but it also features a hefty price tag, right around $3,999.

Nvidia's desktop AI supercomputer now available to the general public By Investing.com

Investing.com -- Nvidia (NASDAQ:NVDA), the world's most valuable company and the face of the AI revolution, is bringing its supercomputing power to the desktop. The company's DGX Spark, a pint-sized AI system that TIME Magazine named one of the "Best Inventions of 2025," will go on general sale Oct. 15. The launch marks another step in Nvidia's push to democratize high-performance computing, bringing data center-level performance to the desktop. At its core is the GB10 Grace Blackwell Superchip, capable of delivering up to one petaFLOPS of performance, roughly a quadrillion floating-point operations per second, a term used to quantify compute power. The Spark integrates ConnectX-7 high-speed networking and Nvidia's full AI software stack, effectively giving startups, researchers, and developers plug-and-play access to industrial-grade compute power. Unveiled at Jensen Huang's GTC 2025 keynote, the DGX Spark was presented as Nvidia's answer to the growing demands of "agentic AI," a new class of reasoning systems that think, plan, and can act autonomously. Holding the small box in the palm of his hand, Huang said DGX Spark and its workstation counterpart, DGX Station, were "created from the ground up to power the next generation of AI research and development." With 20 CPU cores and 128 GB of unified GPU memory, Spark's hardware is tuned for real-world AI work. Nvidia says users can fine-tune models with up to 70 billion parameters, run inference locally, and keep sensitive data entirely on-premise, all without relying on cloud infrastructure. It's also designed to fit neatly into developers' workflows: wired and wireless networking, Bluetooth peripherals, and even the option to link two Sparks into a mini-cluster. "You can literally create your own personal cloud," said Allen Bourgoyne, Nvidia's director of product marketing. Nvidia frames the DGX Spark around four key use cases: * Prototyping next-generation AI agents and chatbots * Fine-tuning medium-to-large models locally * Inference and testing without external dependencies * Data security, keeping information private and on-site The Spark's release comes at a time when the boundary between personal and enterprise computing is blurring. As AI models evolve beyond text and vision to reasoning and autonomy, compute needs are scaling faster than cloud capacity can keep up. Nvidia's bet is that bringing supercomputing closer to the user will be essential to sustain that pace. "There's a clear shift among consumers and enterprises to prioritize systems that can handle the next generation of intelligent workloads," said Michael Dell, CEO of Dell Technologies, underscoring the shift in computational needs as AI rapidly advances. With Spark, Nvidia tightens its grip across the full AI stack, from the H100 and Blackwell GPUs running hyperscale data centers to the compact workstations and edge devices where those models are built. Orders open Wednesday on Nvidia.com, with systems rolling out through partners, select U.S. retailers, and authorized distributors.