{"id":1720717,"date":"2026-10-16T08:36:41","date_gmt":"2026-10-16T08:36:41","guid":{"rendered":"http:\/\/JQ9x9a69R7prL5cQr7Y3ug"},"modified":"2026-10-16T08:36:41","modified_gmt":"2026-10-16T08:36:41","slug":"nvidias-rtx-spark-makes-for-an-impressive-arm-based-gaming-laptop-but-not-one-id-recommend-you-buy-just-for-pc-gaming","status":"publish","type":"post","link":"https:\/\/arcader.org\/news\/nvidias-rtx-spark-makes-for-an-impressive-arm-based-gaming-laptop-but-not-one-id-recommend-you-buy-just-for-pc-gaming\/","title":{"rendered":"Nvidia’s RTX Spark makes for an impressive Arm-based gaming laptop but not one I’d recommend you buy just for PC gaming"},"content":{"rendered":"
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Well, here it is, Nvidia’s all-in-one silicon, the RTX Spark. This is an SoC that’s being promoted as the everything chip; the processor that can be all things to all people. It’s a creator’s chip, it’s an AI-lover’s chip, and it’s a gamer’s chip.<\/p>\n

But all three of those kinds of people do need to have one thing in common: they need to be rather well off, because this is also an expensive chip<\/a>. I spoke about it when the pricing first came to light last week, while I was at the launch event for this Windows-based tech. Even the cheapest of the RTX Spark machines coming our way will cost $2,700, and that will only get you the weaker cores, a paltry 24 GB of unified memory, and a 512 GB SSD.<\/p>\n

That is certainly a limitation, but also completely expected in a time where all things PC cost a ludicrous amount of money, especially devices which are locked into requiring a colossal amount of memory to do their main task. Which, let’s be honest here, is AI.<\/p>\n

The unified memory architecture (UMA) of the RTX Spark is perfectly suited to providing a ton of memory for system resources, but also to be able to switch that over to supporting the GPU when it comes to smashing through local AI workloads which fill up the GPU’s memory pool. It’s the perfect time for Nvidia to release something like this, because local AI is set for a bit of an explosion in popularity (especially when the cloud services start charging real<\/em> money for their services), but it’s also the worst time because this sort of memory-heavy hardware is expensive<\/em>.<\/p>\n

But here it is in front of me, an RTX Spark laptop that looks for all the world like the slimline MacBook-esque Razer Blade<\/a> machines I’ve loved for years. But this is no Blade, this is an Asus ProArt P16 and it’s a stunner. Thinner than the Zephyrus G16<\/a> with a sleek aluminium body and a 64 GB, 20-core RTX Spark N1X chip inside it.<\/p>\n

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\"Asus<\/p>\n<\/div>\n<\/div>

(Image credit: Future)<\/span><\/figcaption><\/figure>\n
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From the get-go I can say that this is not a platform I can really recommend to the average PC gamer<\/p>\n<\/blockquote>\n<\/div>\n

I’ve only been using it a short time, and there is so much to it and so much that is going to change over the next few months that I’m only really going to scratch the surface here. But today I’m going to go over the architecture and the gaming performance, and maybe I’ll give you a little bit about my adventures in agentic AI as a palate cleanser later on.<\/p>\n

Yet, even having spent under a week with an RTX Spark as my daily driver, I think from the get-go I can probably say that this is not a platform I can really recommend to the average PC gamer. It’s too expensive and we’re better served by the gaming laptops already on the market. But, it is an absolutely fascinating, and genuinely impressive platform, and could be a real glimpse at the future of PCs and laptops as a whole.<\/p>\n

Nvidia RTX Spark – architecture<\/span><\/h2>\n
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\"Asus<\/p>\n<\/div>\n<\/div>

(Image credit: Future)<\/span><\/figcaption><\/figure>\n

I’ve been waiting for this moment for a long time. The promise of an all-Nvidia gaming laptop has been a tantalising prospect for years now; even before the company tried to buy Arm itself there were rumours of an SoC that would combine Arm cores with Nvidia’s powerful GPU silicon.<\/p>\n

AMD’s had its own APUs for the longest time, and Intel has been running every facet of a laptop since the dark ages. So it’s no surprise Nvidia wanted a piece of that pie.<\/p>\n

And with Qualcomm taking the initial Windows-on-Arm hit with its Snapdragon Elite and Plus chips, letting the OS evolve around the early teething troubles, the time is now ripe for Nvidia’s platform to come to fruition. <\/p>\n

Actually, there is no Windows-on-Arm anymore as Microsoft is ditching the suffix and just calling it Windows, because Microsoft is all-in here, too. To be honest, I think that’s largely down to the push for local AI and the fact Linux is generally where that lives right now\u2014including the DGX Spark<\/a>, which was the first desktop home of the N1X chip that’s running the RTX Spark. But Microsoft is also helping on the gaming side, making the Prism emulation layer more robust on an Arm platform and also committing to bringing all the anti-cheat apps over that Linux currently doesn’t have.<\/p>\n

Though Epic is working on the Linux side<\/a> of that right now.<\/p>\n

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Nvidia RTX Spark configurations<\/caption>\n
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RTX Spark N1X<\/strong>
(5120-core GPU, 18-core CPU)<\/p>\n<\/td>\n

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RTX Spark N1X<\/strong>
(6144-core GPU, 20-core CPU)<\/p>\n<\/td>\n<\/tr>\n

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CPU cores<\/strong><\/p>\n<\/td>\n

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18 total
(9 Arm Cortex-X925 + 9 Arm Cortex-A725)<\/p>\n<\/td>\n

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20 total
(10 Arm Cortex-X925 + 10 Arm Cortex-A725)<\/p>\n<\/td>\n<\/tr>\n

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Maximum performance-core clock<\/strong><\/p>\n<\/td>\n

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3.9 GHz<\/p>\n<\/td>\n

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4.0\u20134.1 GHz<\/p>\n<\/td>\n<\/tr>\n

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Maximum efficiency-core clock<\/strong><\/p>\n<\/td>\n

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2.8 GHz<\/p>\n<\/td>\n

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2.8 GHz<\/p>\n<\/td>\n<\/tr>\n

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GPU architecture<\/strong><\/p>\n<\/td>\n

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Blackwell RTX<\/p>\n<\/td>\n

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Blackwell RTX<\/p>\n<\/td>\n<\/tr>\n

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CUDA cores<\/strong><\/p>\n<\/td>\n

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5,120<\/p>\n<\/td>\n

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6,144<\/p>\n<\/td>\n<\/tr>\n

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Streaming multiprocessors (SMs)<\/strong><\/p>\n<\/td>\n

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40<\/p>\n<\/td>\n

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48<\/p>\n<\/td>\n<\/tr>\n

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RT core generation<\/strong><\/p>\n<\/td>\n

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Fourth generation<\/p>\n<\/td>\n

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Fourth generation<\/p>\n<\/td>\n<\/tr>\n

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Tensor core generation<\/strong><\/p>\n<\/td>\n

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Fifth generation<\/p>\n<\/td>\n

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Fifth generation<\/p>\n<\/td>\n<\/tr>\n

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Unified memory type<\/strong><\/p>\n<\/td>\n

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LPDDR5X<\/p>\n<\/td>\n

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LPDDR5X<\/p>\n<\/td>\n<\/tr>\n

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Memory interface<\/strong><\/p>\n<\/td>\n

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256-bit \/ 16-channel<\/p>\n<\/td>\n

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256-bit \/ 16-channel<\/p>\n<\/td>\n<\/tr>\n

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Memory interface speed<\/strong><\/p>\n<\/td>\n

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Up to 9,400 Mbps<\/p>\n<\/td>\n

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Up to 9,400 Mbps<\/p>\n<\/td>\n<\/tr>\n

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Peak memory bandwidth<\/strong><\/p>\n<\/td>\n

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Up to 300 GB\/s<\/p>\n<\/td>\n

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Up to 300 GB\/s<\/p>\n<\/td>\n<\/tr>\n

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NVLink C2C bandwidth<\/strong><\/p>\n<\/td>\n

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600 GB\/s bidirectional<\/p>\n<\/td>\n

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600 GB\/s bidirectional<\/p>\n<\/td>\n<\/tr>\n

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Display interface support<\/strong><\/p>\n<\/td>\n

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HDMI 2.1a; DisplayPort 2.0 over USB4<\/p>\n<\/td>\n

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HDMI 2.1a; DisplayPort 2.0 over USB4<\/p>\n<\/td>\n<\/tr>\n

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Process technology<\/strong><\/p>\n<\/td>\n

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TSMC 3 nm (both dies)<\/p>\n<\/td>\n

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TSMC 3 nm (both dies)<\/p>\n<\/td>\n<\/tr>\n

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Configurable SoC TDP<\/strong><\/p>\n<\/td>\n

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45\u201380 W<\/p>\n<\/td>\n

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45\u201380 W<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

The Nvidia RTX Spark SoC packs in all the Nvidia good stuff alongside a whole slew of, what appear to be, relatively off-the-shelf Arm cores. This is all packed in alongside up to 128 GB of unified LPDDR5X-9400 memory.<\/p>\n

First the GPU side. This is exactly the same as we’ve already seen drop in the DGX Spark machine launched around this time last year; it’s an RTX Blackwell-based chip, sporting 6144 CUDA cores across 48 SMs. Effectively, that’s the same core configuration as the desktop RTX 5070 GPU<\/a>, the GB205, although with a chunk less L2 cache, and a necessarily slower clock speed.<\/p>\n

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\"Dave<\/p>\n<\/div>\n<\/div>

(Image credit: Future)<\/span><\/figcaption><\/figure>\n

This is one of the reasons that, despite having the same configuration as the desktop RTX 5070, Nvidia is tempering performance expectations by suggesting it will be more akin to the performance of the mobile <\/em>RTX 5070 (which has fewer SM) in actual gaming performance. <\/p>\n

Aside from the cache difference, and the overall difference in attached system memory (LPDDR5X not GDDR7) we can assume that the layout of the Blackwell SM remains the same as it has done since the architecture first launched. There are still 128 CUDA cores per SM and I would expect 192 fifth gen Tensor Cores and 48 fourth gen RT Cores, as there are in the DGX Spark’s chip.<\/p>\n

The 256-bit memory interface and slower memory means you’re getting 300 GB\/s of memory bandwidth, against the 672 GB\/s the desktop RTX 5070 configuration can offer on its 192-bit bus. But these are slightly apples vs. oranges comparisons because of the platform the RTX Spark is sitting in, the unified memory architecture (UMA) it’s surrounded by, and the CPU companion it has.<\/p>\n

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\"Nvidia's
Nvidia<\/small><\/figcaption><\/figure>\n
\"Nvidia's
Nvidia<\/small><\/figcaption><\/figure>\n<\/figure>\n

Which, although Nvidia is calling it a Grace CPU, and custom-made with MediaTek, does still just sport some off-the-shelf Arm cores. But a lot of them, to be fair. There are 10 ‘performance’ Arm Cortex-X925 cores and 10 ‘efficient’ Arm Cortex-A725 cores and they mark the peak of Arm’s 2024 lineup.<\/p>\n

As far as we know, however, there hasn’t been any additional work done with these cores to help bolster the necessary emulation to be able to run the x86-focused workloads that are going to be thrown their way. This is the step Apple has taken with its own Arm cores, making sure they’re designed from the ground up to take care of such things.<\/p>\n

There is also a second, cut-down RTX Spark SoC. That comes with 5120 CUDA cores across 40 SMs (so a commensurate reduction in Tensor cores and RT cores, too) and with 18 Arm CPU cores, meaning one less of each of the performance and efficient cores.<\/p>\n

Nvidia RTX Spark – Gaming performance<\/span><\/h2>\n
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