RTX Spark vs DGX Spark: Windows Laptop vs Linux Dev Box (2026)

RTX Spark and DGX Spark run the same chip. That's the whole premise of NVIDIA bringing GB10, the Grace Blackwell Superchip it built for DGX Spark, to Windows laptops and mini-PCs under the RTX Spark name: identical silicon, a completely different machine wrapped around it. What a buyer gets for choosing Windows over DGX Spark's Linux box is a real trade, not a free upgrade, and most of the coverage so far compares specs sheets without running the power-budget math that actually decides whether that trade is worth it.

TL;DR: RTX Spark vs DGX Spark

• Same chip: RTX Spark laptops run the identical GB10 Superchip as DGX Spark: a 20-core Grace CPU plus a 6,144-core Blackwell GPU, up to 128GB unified memory, and up to 1 petaflop FP4.
• Power gap: RTX Spark laptops run 45-80W; the desktop N1X variant matches DGX Spark's 140W rating, but Microsoft's own Surface RTX Spark Dev Box chassis caps that at 100W sustained.
• Networking: DGX Spark, shipping since late 2025, has built-in ConnectX-7 200GbE for clustering two units; RTX Spark drops it entirely.
• Software: DGX Spark runs native Linux CUDA; RTX Spark depends on the Windows Arm64 CUDA path NVIDIA shipped as a developer preview on July 19, 2026, alongside driver 616.00.
• Past 128GB: Spheron rents H100 instances on-demand by the minute, at a live rate of $2.64/hr as of 17 Oct 2026, for workloads that outgrow either desk box.

RTX Spark vs DGX Spark: What's Identical and What Windows Changes

Start with what doesn't change. That is, core for core and gigabyte for gigabyte, the same GB10 Grace Blackwell Superchip inside DGX Spark. NVIDIA didn't design a smaller or cut-down chip for Windows; it put the same part in a different box and changed the operating system around it.

What changes starts with the OS itself. DGX Spark runs DGX OS, NVIDIA's Ubuntu-based Linux distribution, giving it what Enverge's direct comparison calls datacenter-identical CUDA: the exact same CUDA stack a developer would use on a rented H100 or an on-prem DGX server, with no translation layer in between. RTX Spark runs Windows 11 on Arm instead, which means GPU workloads reach the chip through a separate native Arm64 CUDA path NVIDIA had to build specifically for this launch, while ordinary x86 Windows applications run through Microsoft's Prism emulation layer.

The other two changes are networking and power, and they matter more than the OS swap for anyone planning real inference or fine-tuning work. DGX Spark includes a built-in ConnectX-7 200GbE port specifically so two units can be clustered. RTX Spark drops that port entirely, per the same Enverge comparison. And RTX Spark's power envelope depends entirely on which chassis you buy: laptop and mini-PC variants run at 45-80W, while NVIDIA's desktop N1X configuration runs at 140W, according to StorageReview's pre-order coverage. Same chip, three real differences, and only one of them (the OS) is cosmetic.

NVIDIA RTX Spark Specs: GB10 Silicon, CUDA Cores, Memory, and the 1-Petaflop Number

RTX Spark's GPU is NVIDIA's GB10 Grace Blackwell Superchip: a 20-core Arm Grace CPU paired with a 6,144-core Blackwell RTX GPU, up to 128GB of unified LPDDR5X memory, and up to 1 petaflop of FP4 AI performance with sparsity, per NVIDIA's RTX Spark product page. That's the identical chip DGX Spark ships, and it's powerful enough on paper to put RTX Spark's GPU core count in the same bracket as a current desktop gaming card, with real caveats on power and memory that the raw number hides.

That 6,144 CUDA core figure answers a question people keep asking in comparison threads: what discrete GPU is RTX Spark actually like? On paper, RTX Spark's GPU is the RTX 5070's compute class. In practice the comparison inverts fast once you look past the core count: the RTX 5070 runs at a 250W Total Graphics Power with 12GB of its own dedicated GDDR7, while RTX Spark runs that same core count anywhere from 45W to 140W depending on the chassis, and shares a 128GB pool with the CPU instead of owning dedicated VRAM. Matching core counts on two very different power and memory budgets is a starting point for a comparison, not an answer to it.

For the discrete-GPU side of that same Blackwell generation, our NVIDIA RTX 5090 specs breakdown covers the desktop flagship's CUDA core count, GDDR7 bandwidth, and FP4 tensor throughput in full.

What You Give Up Running Windows: ConnectX-7, Prism Emulation, and the 140W Gap

Three things disappear or get harder when you pick RTX Spark over DGX Spark, and all three show up in the fine print rather than the headline spec sheet.

ConnectX-7 clustering is gone. DGX Spark's built-in ConnectX-7 200GbE port lets two units pool their memory into one larger working set, a feature our DGX Spark 64GB clustering guide covers in detail. RTX Spark has no equivalent port. A developer on the NVIDIA forums summed up the trade plainly when discussing the two platforms: "RTX drops the ConnectX-7 interface, so it will probably be cheaper, but it loses the fast connection between nodes." There's no RTX Spark equivalent to clustering two DGX Spark units into a 128GB pool at double the bandwidth; on Windows, one unit's memory ceiling is the ceiling.

The CUDA path on Windows Arm64 is new, and NVIDIA says so itself. RTX Spark's software foundation shipped only months before the hardware: NVIDIA released CUDA Toolkit 13.4 as a developer preview with native Windows Arm64 compilation support, per Hardware Busters' coverage of the release. That split matters because Prism, Microsoft's x86 emulation layer, only translates user-mode x86 code; it doesn't emulate drivers, which is exactly why NVIDIA had to build a separate native Arm64 CUDA path rather than leaning on emulation for GPU workloads. And that path arrived with documented rough edges: NVIDIA's own release notes for the preview flag pageable-memory transfers performing below expectations, some PyTorch build workflows triggering GPU timeouts or system restarts, and Nsight Copilot being entirely unavailable on Windows Arm64 in this release, with NVIDIA stating directly that the preview "isn't intended for production deployment, certification or benchmarking." A reader buying RTX Spark for serious CUDA development work is buying into a stack NVIDIA itself hasn't certified as production-ready yet.

The power gap is real, and it's bigger than the spec sheet number suggests. NVIDIA rates the desktop N1X variant of RTX Spark at 140W, matching DGX Spark's published GB10 TDP exactly. But explainx's breakdown of the power envelopes notes that Microsoft's own Surface RTX Spark Dev Box, one of the desktop systems meant to hit that 140W figure, quotes a chassis thermal envelope of only 100W sustained. And that's the desktop variant. Laptop and mini-PC RTX Spark systems run at 45-80W, a third to a little over half of what DGX Spark is rated for.

Tokens Per Second on the Same GB10 Chip: Laptop Power Limits vs 140W Desktop

The only published tokens-per-second numbers on GB10 are DGX Spark's, measured at its full 140W rating. LMSYS's benchmark of the chip found Llama 3.1 8B FP8 decoding at 20.5 tokens per second single-stream, scaling up to 368 tokens per second at a batch size of 32, with GPT-OSS 20B in MXFP4 decoding at 49.7 tokens per second. Those figures, and the batching story our DGX Spark vs RTX 5090 tokens-per-second comparison walks through in more depth, are the baseline for what this chip does when it's allowed to run at full power.

Nobody has published an equivalent benchmark on RTX Spark's laptop configurations yet, and that's worth sitting with rather than guessing past. The relationship between power and sustained clock speed on this same silicon is already documented from the DGX Spark side: our DGX Spark thermal throttling piece measured SM clock decay and real-world temperature behavior on the exact 140W-rated chip that DGX Spark ships, and found meaningful throughput variance even at that higher power ceiling. A laptop running the same GB10 chip at a third to half the power has less thermal and clock headroom to work with before it starts trading speed for staying cool, and a 45W chassis simply cannot sustain the same clocks a 140W one can, independent of software. There's also a second variable nobody controls for yet: NVIDIA's own release notes for the Windows Arm64 CUDA preview explicitly say it "isn't intended for... benchmarking," so any early RTX Spark tokens-per-second number that does surface before the driver stack matures should be read as a snapshot of a preview build, not a settled figure for the hardware.

One number does run in RTX Spark's favor: some laptop OEMs are specifying faster LPDDR5X memory grades than DGX Spark's 273 GB/s, with figures landing near 300 GB/s according to the Enverge comparison cited above. Faster memory helps the bandwidth-bound decode phase regardless of power budget, so RTX Spark's real-world throughput gap against DGX Spark, whatever it turns out to be, likely won't scale down in exact proportion to the wattage difference.

Here's the actual test to run once a unit is on a desk, rather than guess at it from a spec sheet: pull the same Llama 3.1 8B Instruct FP8 checkpoint LMSYS used for the DGX Spark numbers above, serve it with vLLM on the RTX Spark box at single-stream and batch-32, and log decode tokens/sec at each setting. Write down the chassis TDP printed on the laptop or mini-PC alongside the result. That single afternoon of logging turns this post's power-budget argument from a prediction into a number anyone can check against the 20.5 tok/s and 368 tok/s DGX Spark baseline, and it's the same checkpoint-and-harness pairing LMSYS already published, so the comparison needs no new tooling to run.

Is RTX Spark Good for Gaming? DLSS 5, Reflex 2, and the RTX 5070-Class Claim

Yes, by design. NVIDIA markets RTX Spark laptops as Game Ready, built around DLSS 5, Reflex 2, fourth-generation RT cores, and fifth-generation Tensor cores, with access to a growing catalog of PC games on Windows on Arm that NVIDIA's own product page names as including titles like Fortnite and Alan Wake 2. That's a meaningfully different pitch than DGX Spark, which NVIDIA has never positioned as a gaming machine at all.

The RTX 5070-class core count carries over here too, with the same caveat. A game running natively on Windows on Arm, or translated well through Prism, gets a GPU with the same 6,144 CUDA cores as a desktop RTX 5070. What it doesn't get is that card's 250W power budget or dedicated 12GB of GDDR7; a 45-80W laptop chassis is going to clock that silicon far more conservatively than a desktop card ever would, and frame rates will land somewhere below a desktop RTX 5070 as a direct result, not because the core count is different. How far below depends on the specific chassis TDP and whether a given title runs native Arm64 or through emulation, neither of which NVIDIA has published comparative benchmarks for yet.

When You Outgrow 128GB on Windows and Need to Rent a Cloud GPU

Both machines cap at the same 128GB unified pool, and both hit the same wall once a workload needs more: a dense model north of what fits in that memory, more concurrent users than one GB10 chip can batch through, or uptime guarantees neither desktop box offers out of the box. DGX Spark at least has a hardware path past that ceiling. Its ConnectX-7 port lets two units cluster into a combined pool, which our DGX Spark fine-tuning guide uses to work out exactly which LoRA and QLoRA jobs fit inside that memory and which don't. RTX Spark, having dropped ConnectX-7 entirely, has no equivalent. A second RTX Spark unit doesn't combine with the first one; it's just a second, separate 128GB box.

Buying more desk hardware to chase memory headroom is also getting more expensive, not less. ServeTheHome's Patrick Kennedy, writing about a separate GB10 price increase, put the underlying pressure plainly: "we are paying $2000 for 64GB ECC RDIMMs, so pricing logically must increase". Memory prices feeding directly into GB10 device pricing means the "just buy a second unit" path only gets costlier over time, on both the DGX Spark and RTX Spark side of this comparison.

That's where renting makes more sense than buying a second box. Spheron rents H100, H200, B200, B300, and A100 GPUs on-demand, billed per minute after a 20-minute minimum with no contracts, so a reader can test whether a model genuinely needs more than 128GB before committing to more hardware. For a model that needs more memory per GPU than an H100's 80GB offers, an H200 instance brings 141GB of HBM3e on a single card, more than either desk box's unified pool and on faster, dedicated memory. Our DGX Spark and GPU cloud pipeline guide covers the practical handoff: developing and validating on a desk unit, then containerizing the same stack to move to a rented cloud GPU once concurrency or uptime requirements outgrow what one box can hold. Not every workload belongs there, though. Rack-scale NVL72 capacity like GB200 and GB300 is reservation-only on Spheron, not an instant on-demand rate, so a reader specifically wanting that scale available the moment they're billed should expect NVIDIA's own GB200 and GB300 systems through a reservation, not an hourly meter.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 17 Oct 2026; RTX Spark and DGX Spark prices and specs reflect NVIDIA's and its partners' most recently published figures as of this post's date and may have changed. Check current GPU pricing → for live rates, and each OEM's own listing for current RTX Spark and DGX Spark availability.

Once a GB10-based desk box, Windows or Linux, hits its 128GB ceiling, Spheron's on-demand H100 and H200 instances pick up the workload the same day, no second unit required.

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