Does this person know that this is the same GB chip in the DGX Spark? It isn't some proposed thing, it's a chip loads of people have on their desk right now, and there are endless benchmarks of it.
Decent single core (a long ways from Apple level, but decent), but it makes up for it in cores to provide M5 level performance, CPU wise. Memory bandwidth it is kind of starved, at 1/6th many GPUs.
They got Microsoft to customize Windows for the RTX Spark, and will likely have to brutally throttle it when running as a laptop (it's literally a 140W TDP chip), and that's neat. It's going to be a very expensive laptop.
Is it really unified memory? AMD Strix Halo is "unified" but you still have to allocate memory separately for cpu vs gpu. Apple Silicon is true unified memory.
"I am not sure how many people will run AI models locally. It still seems like a niche application to me. However, it will make decent machines to play video games."
I don't know who will be the winner but with some of the recent releases from gemma it seems more probable that you may run some models locally if only from a cost perspective, not even considering business security. Not sure how this type of architecture would make for good gaming though, puts into question the whole statement.
"Ranked in the top 2% of scientists globally (Stanford/Elsevier 2025) and among GitHub's top 1000 developers" - side note but this guy puts this everywhere, gives me probably the inverse of what he is marketing for.
No, it stems from a lineage of Tegra chips that pre-date the M-Series.
This chip was called GB10. One of its predecessors, GV10 was shipped in 2018.
It was a 256 bit, unified-memory system on a chip with a Volta GPU and 12 ARM Cores. GB10 is a 256 bit, unified-memory system on a chip with a Blackwell GPU and 10+10 ARM cores.
The interesting part to me isn't really the Cortex-X925 vs AVX-512 comparison, but Nvidia trying to make the GPU the center of a Windows PC rather than an add-in card
while unified memory may offer better performance than unsoldered DDR system memory, it still won't be as great as 1.8TB/s bandwidth on high end consumer GPUs right now.
nvidias master plan may be making it the new normal to have "only" 400GB/s bandwidth, thus gatekeeping local model usage further behind "more memory but not as fast as the cloud can do it"
I have been somewhat surprised at the lack of commentators observing that this is Microsoft and above all NVIDIA launching a device that is fundamentally at odds with the metered cloud model of AI.
When you look at the other announcements and murmurings (better offline BYOK for Copilot, talk of an unmetered AI future) I think it’s clear that these two firms understand that cloud-only AI is not sustainable or inherently in their interests. But their willingness to undermine OpenAI with a product like this is notable.
For me this is a push to segment the market into consumer and industrial grade RAM. Even NVIDIA and MS are not stupid enough to think they can keep going with RAM prices exploding. Consumers need hardware to subscribe to their AI stuff.
LLMs will get bigger and even with 128GB (that many wont saturate), you wont run future frontier models. For LLM vendors and integrators it's a handy thing to move lower quality inference to the consumers.
Also running local doesn't have to mean that the models have open weights. MS will likely start to distribute closed models at scale once the hardware is there.
Don't want to be too harsh, maybe I'm missing something, but the CPU is at least 2 years old, internally it has been a complete shitshow and that's a minor hiccup when compared to the firmware and software situation.
It's an interesting "newcomer" and the more the better but calling this a "beast" and a "game changer" is ridiculous to say the least.
This feels fluff to me on the part of the author (whose work I don’t want to trivialize) but I don’t think they’ve actually looked deeper than a paper spec sheet on this.
1. Yes it has the same number of cores as a 5070 mobile. It’s also running at a shared peak of 2/3 the bandwidth and a shared peak of 2/3 the TDP. The GPU by itself will likely perform at half the dedicated units performance
2. Apple may not have SVE2 but they do have the AMX (private) and SME. I don’t see why he thinks the SVE2 will give him more performance than the SME.
3. He mentions a single core type but doesn’t mention the total makeup. We already have known for a year how the DGX Spark compares to Apple chips. For CPU it’s roughly equivalent to an M3 Pro and for GPU compute (not rasterization) it’s between an M4 Pro and M4 Max without considering bandwidth.
The real advantage to these is that they run CUDA. That’s it. Otherwise when they launch they’ll be 2-3 generations behind where Apple is and 1 gen behind AMD.
The other super power of the DGX Spark was the NIC for pairing them together. But that’s been removed here too.
A RTX Pro 6000 has ~24K 5th generation tensor cores, I'm guessing this would then be 1/4 of the count but 6th generation? Wasn't clear from the images.
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[ 2.3 ms ] story [ 69.2 ms ] threadI'd say this relates directly to the cost of running AI models remotely.
And we won't know what the actual cost will be until AI vendors recover the huge pile of cash they've dumped into development (plus interest).
Decent single core (a long ways from Apple level, but decent), but it makes up for it in cores to provide M5 level performance, CPU wise. Memory bandwidth it is kind of starved, at 1/6th many GPUs.
They got Microsoft to customize Windows for the RTX Spark, and will likely have to brutally throttle it when running as a laptop (it's literally a 140W TDP chip), and that's neat. It's going to be a very expensive laptop.
Bill Gates had a quote some years ago...
People have still not learned how fast we improve our tech and how much cheaper thing gets I guess :)
I don't know who will be the winner but with some of the recent releases from gemma it seems more probable that you may run some models locally if only from a cost perspective, not even considering business security. Not sure how this type of architecture would make for good gaming though, puts into question the whole statement.
"Ranked in the top 2% of scientists globally (Stanford/Elsevier 2025) and among GitHub's top 1000 developers" - side note but this guy puts this everywhere, gives me probably the inverse of what he is marketing for.
Tech companies have strangled their own market.
nvidias master plan may be making it the new normal to have "only" 400GB/s bandwidth, thus gatekeeping local model usage further behind "more memory but not as fast as the cloud can do it"
https://nvidianews.nvidia.com/news/nvidia-microsoft-windows-...
I have been somewhat surprised at the lack of commentators observing that this is Microsoft and above all NVIDIA launching a device that is fundamentally at odds with the metered cloud model of AI.
When you look at the other announcements and murmurings (better offline BYOK for Copilot, talk of an unmetered AI future) I think it’s clear that these two firms understand that cloud-only AI is not sustainable or inherently in their interests. But their willingness to undermine OpenAI with a product like this is notable.
LLMs will get bigger and even with 128GB (that many wont saturate), you wont run future frontier models. For LLM vendors and integrators it's a handy thing to move lower quality inference to the consumers.
Also running local doesn't have to mean that the models have open weights. MS will likely start to distribute closed models at scale once the hardware is there.
A powerful new chapter for Windows PCs, accelerated by Nvidia RTX Spark
https://news.ycombinator.com/item?id=48352693
Nvidia RTX Spark
https://news.ycombinator.com/item?id=48352939
It's an interesting "newcomer" and the more the better but calling this a "beast" and a "game changer" is ridiculous to say the least.
Then there is the price..
1. Yes it has the same number of cores as a 5070 mobile. It’s also running at a shared peak of 2/3 the bandwidth and a shared peak of 2/3 the TDP. The GPU by itself will likely perform at half the dedicated units performance
2. Apple may not have SVE2 but they do have the AMX (private) and SME. I don’t see why he thinks the SVE2 will give him more performance than the SME.
3. He mentions a single core type but doesn’t mention the total makeup. We already have known for a year how the DGX Spark compares to Apple chips. For CPU it’s roughly equivalent to an M3 Pro and for GPU compute (not rasterization) it’s between an M4 Pro and M4 Max without considering bandwidth.
The real advantage to these is that they run CUDA. That’s it. Otherwise when they launch they’ll be 2-3 generations behind where Apple is and 1 gen behind AMD.
The other super power of the DGX Spark was the NIC for pairing them together. But that’s been removed here too.
It's just a personal computer. It normally runs multiple operating systems just fine.
Windows PC sounds like people talking about tech who are either payed by M$, or embed pictures into Word documents to send them.
Nobody has to kill the fun those OS agnostic machine allow, by artificially bind them to a shitty OS.
A RTX Pro 6000 has ~24K 5th generation tensor cores, I'm guessing this would then be 1/4 of the count but 6th generation? Wasn't clear from the images.