Not sure there's a strong use case for one of these $6K laptops yet, considering you can get a mini PC running AMD Ryzen 495 or 395 with 96, 128 and 192 GB of memory for similar amount of money and connect remotely from a cheaper/smaller/more battery-efficient laptop. Microsoft also has this same chip in non-laptop form factor: https://www.microsoft.com/en-us/surface/devices/surface-rtx-....
Coding while flying is often the use case people bring up, but the power brick these laptops come with sometimes trip the plane's power plug in coach and you can't run local AI on battery for long. Also, over time planes will get better connectivity (Starlink etc.) and that particular full-offline scenario will go away.
For gaming, there are better options with dedicated GPUs like the Asus Zephyrus and similar.
The strong case is pretty much only if you can't wait another 3-4 years for ram prices to come back down.
Memory makers are responding with a huge build-out. Building a fab shell and cleanroom takes years, which is why the crunch has lasted so long, but many of these projects are now well underway. New capacity is what ended past memory shortages. New factories going in:
SK hynix
M15X, Cheongju, Korea: first wafers early 2026 (reported), ramping through 2027
Yongin Y1, Korea: first cleanroom Feb 2027 (company target)
Yongin Y2, Korea: groundbreaking planned July 2027, first cleanroom June 2029
Micron
ID1, Boise: first wafer output mid-2027
Tongluo, Taiwan: an existing PSMC cleanroom it bought for $1.8B, with meaningful shipments mid-2027
ID2, Boise: late 2028
Hiroshima, Japan: broke ground July 2026, with initial output late 2028
Clay, New York (up to 4 fabs, $100B): broke ground Jan 2026, with first output in 2030
Samsung
P5, Pyeongtaek: mass production 2028 (reported)
Yongin national complex: site work began Oct 2026, with the first fab targeting Oct 2029 (reported; Samsung hasn't given a date)
Nanya
Fab 5A, New Taipei: 10nm-class EUV DRAM, with wafer starts in 2H 2027 (reported)
Tesla / SpaceX (Terafab)
Research fab at Giga Texas, Austin: broke ground April 2026. There's no official output date.
Grimes County, Texas (logic, memory, packaging; Phase 1 over $16.8B): land clearing since Aug 2026. Texas filings say commercial operations in 2028, but SemiAnalysis estimates late 2029.
The first real supply relief is 2027, a second wave comes in 2028–29, and a lot of that new capacity is aimed at HBM and server memory, so consumer DDR5 and GDDR will likely ease last.
Sources: Micron FQ4 2026 call, the SK hynix newsroom, TrendForce, Texas JETI filings via ENR, and Tom's Hardware/SemiAnalysis.
And maybe 2027 is a truly optimistic relief on some level,
> a second wave comes in 2028–29, and a lot of that new capacity is aimed at HBM and server memory, so consumer DDR5 and GDDR will likely ease last.
'Consumer DDR5' has a pretty big set of knock-on effects though.
Unless you are working at a 'tech' company, the norm is that even developer machines will be constrained on memory usage for a given workload[0][1]. IOW companies trying to keep procurement in check wind up looking at procurement cost vs lost productivity.
[0] - Modern infosec and corporate software, at least where I'm at, seems to be vaguely more efficient than electron apps, IOW I can reboot, open teams and my IDE, after 4GBish of my IDE and 1GB of a fresh Teams instance I'm already around 16 of 32GB.
[1] - I had to fight hard to get 32GB as a developer baseline at my org. They wanted 16GB. Ironically the JVM based devs get 64GB but still have slowdown issues on biuld...
> Unless you are working at a 'tech' company, the norm is that even developer machines will be constrained on memory usage for a given workload
Honestly I think most of us are just spoiled kids crying when our expensive toys are being taken away. If memory cost 10x more to manufacture than it does now, we’d just have 10x less ram in our computers and our software would be written to deal with that. We’ve done it before. My first computer had 4mb of ram and it ran windows 3.1 just fine. We still had working compilers. If ram were 10x cheaper before the supply shock, I think we’d be crying that companies won’t buy us 320gb ram in our laptops. The java and rust compilers would somehow be even less ram efficient. Firefox and chrome would still happily use it all to run adware across the same 50 tabs I want to keep open.
Theres good reason for LLMs to need that much ram. But very little else we do on our computers needs anywhere near as much ram as we use. Have faster computers made video games more fun in the last two decades? I don’t think so.
The supply crisis is annoying. But I’m also a little glad there’s a reason to push back on fat, lazy software.
I work at a place which offers a well-known operating system and also sells hardware which runs said OS. This is exactly what we have done, because the hardware releases this year have had to come with a quarter less memory. There is an internal effort to reduce memory consumption by a quarter at all levels of the platform, from the kernel, to the drivers and user-mode libraries, to the toolkits and user-mode applications. This has been remarkably successful.
I’m not surprised! The amount of wasted memory and compute in modern software is astounding.
If you’re a developer, try promoting Claude to profile and optimise a piece of some piece of software. I routinely see 20-40x improvements with a single prompt and a few hours of opus credits.
Thanks for your work by the way. At a large scale, it is much more efficient for some software developers to do a bit of belt tightening in their code than ask millions of customers to invest in bigger computers. Globally the savings are enormous.
Cheers; we try, but I wish we didn't have a memory crunch to get this effort going. I suspect at the end of all this we will have more memory-efficient code.
> try promoting Claude to profile
Oh, the irony. Claude is itself an Electron app and lags horribly.
At this point, all these agent apps—Claude, Copilot, ChatGPT—are just calling remote APIs. Why not train them on the native toolkits for each OS (Win32/WinUI3, Cocoa/SwiftUI, GTK, Qt, Jetpack Compose), and make the experience better for users?
Realistically, GOG probably is trying to accommodate for the OS running the game. So it is more for the spec of the PC, not the RAM the game itself needs
Well, so its not just bloated software that makes software bloated, but bloated facts, too. Users will see specs like that and go "well, I need to upgrade my machine from 2mb to 8gigs now, d'oh!" ...
I bet these factories exclusively build DDR6 and whatever GDDR is up to next, rather than DDR5, since that was already scheduled for 2027 anyways. Hopefully they don’t immediately shutter their DDR5 facilities rather than cut into their 90% operating margins :)
Maybe I'm missing something but didn't micron exit the consumer DDR market for HBM? I don't see a rollback from that. When you say DDR will ease last, in what reality would memory makers go back to a market they left behind?
So do you think there will be a big crash and memory will get really cheap in 2030, or do you believe that AI demand is strong enough to sustain all this buildout?
It seems like this list also doesn't account for China and state sponsored memory industry (which will be more immune to a downturn)
The core design is also approaching 3 years old at this point, so it’s not really even that competitive with the latest stuff out of the other chip makers. It’s fine, but nothing to write home about. Certainly not worth the crazy premium Nvidia is charging for it.
I don’t even really think it’s “fine.” It’s hard to even understand who they are for. Just Josh Tech on YouTube did a great review of these laptops as they’re hitting the market.
Good battery life is probably the biggest upside, but the Intel Core Ultra Series 3 Zephyrus G16 is just about as good (more on that alternative later).
The downsides are numerous:
- Middling GPU performance compared to discrete RTX mobile GPUs. You’re barely trading blows with an 5070Ti with the top end Spark chip. And yes, Nvidia markets RTX Spark for gaming, so we need to judge them in that context, too. Windows on ARM isn’t amazing for gaming for various reasons including lack of anti-cheat compatibility.
- Compatibility and/or performance issues on Windows ARM. You’re running a lot of stuff non-native
- As you mentioned, the CPU cores are nothing to write home about with very mid single core performance.
- Suffers greatly from “why am I not buying a MacBook Pro?” syndrome
- Poor/no Linux support, in a world where when Linux and Mac get the most attention for AI workflows.
I think there may be a case for being a good performance value for AI training, but for local inference I’m not seeing how that makes sense. Heck, for local inference I think it’s better to run a workstation as a server and then use the laptop as a client. You benefit from a better hardware value as well as putting less of the strain on the laptop, saving battery life. The fact that it’s plugged in means you can run long-running jobs and scheduled tasks, which is often the strength of local AI. The cost of one of these RTX Spark laptops will get you a dual R9700 inference workstation if you’re smart about the build.
You’ve also got projects like Strata that are going to define trends for how people run local AI. Why bother with $6000 integrated graphics machines or trying to squeeze quality models into one or two expensive 24-32GB GPUs when you can get 60 tok/s on Qwen 3.8 Flash Next on a 16GB RX 9060XT ($500) with 64GB of DDR4 RAM?
Want to do local inference on a laptop? Grab a Zephyrus G16, get the 64GB/RTX 5080 spec, run Strata, then as a bonus you can run AAA games at full blast. This costs less than $5000.
Waiting ~1 year for the M7 Pro chip on MacBook Pro models is another option that is almost certainly a better idea if the rumors about that chip are true.
I’m struggling to find a way to find a worse value buying something, anything that isn’t an RTX Spark. For that money there are just so many alternatives.
> Suffers greatly from “why am I not buying a MacBook Pro?” syndrome
Well, reading the reviews it's clear these are filling the MacBook Pro gap in windows laptop lineups.
And the answer to "why am I not buying a MacBook Pro?" for many people is because it's a MacBook pro. I've never owned Apple hardware in my life and have no desire to do so now but I do wish there was equivalent quality non Apple hardware (for me that I can run Linux on, but for many people, Windows would be fine). If the price was lower, I'd be in the market for one of these.
So far the only options has been gaming/creator laptops with dedicated GPUs. I currently have an MSI Ge66 and it's a great laptop in many ways but it runs so hot, and the fan is so loud. I'd pay a premium (but not $6000!) to reduce that. Also, the unified memory is a big draw for both Blender and local AI.
I guess this is technically true, as the specs/prices seem to line up closely with a MacBook Pro.
Although, the thing is, for the AI tasks advertised, the MacBook Pros have faster memory, so token decode runs faster.
Then by using a Mac you also scuttle the issue of "My platform has a low rate of adoption" that Windows on ARM has had. That issue will get better over time but in the present it's still an issue.
As for the dedicated GPU gaming laptop market, you'd be surprised how it's evolved since the release date of your MSI Ge66. I mentioned the 2026 Zephyrus G16 specifically because it is a laptop with very few compromises, including heat and noise. If you don't specifically need a huge amount of GPU-addressable RAM, and you're just looking for good performance in games and graphics-intensive applications, it is a clearly better buy than these RTX Spark systems. As a bonus, being an X86 system you'll have a much easier getting Linux going on that one.
So, yes, it's good that there is another MacBook Pro alternative, but I think the issue remains that it's unclear who exactly the buyer persona is going to be. I think a lot of people in the venn diagram are going to just go with a MacBook Pro.
This part is more speculation on my part, but if Apple brings out the M7 Pro/Max chips on the rumored 2027 schedule, I think they will really blowpast RTX Spark at a time when it will take Nvidia years to have an equivalent answer.
It will also be curious to see if AMD can catch back up Medusa Halo launching next year. If that is anything close to RTX Spark it will sidestep the Windows on ARM issue entirely and look like a pretty attractive alternative. Hopefully AMD actually gets it put into a wide selection of laptops this time around.
> If you don't specifically need a huge amount of GPU-addressable RAM
Well, even when I bought the Ge66 I would have loved unified memory for Blender but it's now even more important since the graphics card manufacturers are being so stingy with it and I want to run local models in addition to graphics apps and games.
TBH I think the "separate memory for GPU model" is dead, it's just taking a long time for the industry to catch up. So I'm going to live with my laptop for a few more years until it does.
I think the ones to watch on this area for non-Apple devices is Qualcomm. They just need better iGPUs and Linux support and they'll have genuine Apple M competition.
Lack of anti cheat is rather outdated perception of Windows on Arm. Most popular competitive games like League of Legends have native arm versions now.
The problem is more holistic. It’s a buyer persona problem.
Even if your game is running natively and your anti-cheat is supported, you’d have gotten better performance on a Zephyrus G14/G16 for less money.
If you want to do local AI a MacBook Pro is either keeping up or going faster (faster memory bandwidth, token output writes faster), with faster single-core CPU performance as a bonus for general purpose snappiness.
In my eyes, the only buyer left is someone who can’t stand a MacBook Pro, or needs Nvidia/CUDA and a lot of VRAM.
To be more generous to the RTX Spark platform, it does seem to be priced head-to-head with Apple. I just think it has an uphill battle.
If it's anything like my DGX Spark, the "premium" is almost 100% just the memory cost, not the GPU or ARM core.
10 months ago when I got my Spark it was competitive in price with a similarly spec'd out Mac Studio. As RAM prices have gone even more parabolic, so has its price. And so has the Mac's. And the Strix Halo competitors.
I feel like even if a next generation of chips dropped tomorrow, it'd be a while until they can scale up production and actually start selling products you or I could realistically purchase and by that time this new generation chip would already by a couple of years old with something much better and powerful in the final stages of design.
That when civilization falls, and we have to go to techno agrarian farming, like in Interstellar, the person with the RTX isn't the one that's going to be left behind, it's the person with the cloud account that owns nothing.
The author’s discussion of their personal use case starts on the seventh sentence in TFA. And frankly, it sounds like a pretty solid use case:
“Being able to run things locally and mess around with RL, quantisation and fine-tuning on a laptop would be sick. I already publish my own GGUF quants on Hugging Face, with over 160,000 downloads in the past month.”
This is similar to "Why buy powerful gaming laptop when you can build a gaming PC and a cheap laptop to remote into it". That was my setup years ago, and there are a lot of nuance things i hadn't taken into account.
For example, network is not always available and stable; File transferring is limited by connection speed, hardware passthrough is basically nonexistence; Dealing with power management (Unexpected power loss) and so on.
I bet they all can be solved(to some bearable degree), but at that point, just bite a bullet and get a powerful laptop.
The difference is that streaming zero latency 4k video is incredibly difficult and always results in serious compromises. For AI programming, the end output is very small, not latency sensitive very easy to stream over the internet.
I'm sure you can come up with some scenario where you are programming in the wilderness with no internet connection but the market for that has to be in the tens of people.
The Nvidia based one uses the memory for the GPU allowing you to run AI models on the GPU, the system you're talking about has no such setup. These are Nvidia powered laptops that do what Apple does with their laptops where the system and GPU VRAM are shared as needed, except the idea here is that Nvidia designed these to perform very well for local inference, at least, that's how I understand it.
Depending on what you are doing you may find all the models are written for CUDA though. I'm playing with gaussian splatting and it's like that. Codex is doing a good job porting stuff to Metal, but it would be easier & faster if I had Nvidia hardware to just run the models natively.
Not everything with AI is LLMs running on llama.cpp.
Maybe you have some malware or they have some targeted thing running, clicking the testimonials does nothing at all for me. I'll request to remove it just in case.
Yes, but in practice it’s more interesting than that
The massively lopsided compute makes speculative execution techniques (MTP/DFlash etc) way more effective, which makes modern MoE models quite usable
I’m running Qwen 3.8 Flash Next with Nota AI’s conservative NVFP4 quant at 30tk/s lowest decode, usually more around 45-55 for coding, and with prompt processing at a solid 2000tk/s
And that’s without very optimised kernels or anything, mostly standard vLLM
You could just code on a flight.. No need for llm. Id your brain is incapable of writing code without internet/llms you could write your specs/prompts for when you land.
Why? Flying business everywhere is substantially more expensive than a one time cost of $6k for a laptop. A single business ticket anywhere overseas matches or exceeds that in most cases.
Last first class ticket I bought was US$13k, or a bit less than $500/hr in the air for the trip I took. A 6k laptop that I'm gonna use for 8-12 hrs/day for a couple of years is a rounding error in comparison.
What? I have spent $10k on a computer but would never fly business or first class, that shit is way too expensive. Two trips would buy a new $10k computer!
but you're in a position to understand now, that people who do fly business everywhere, can also afford one of these laptops for a fraction of what they spend flying. that's the use case.
my style of discussing things (let's call it "give credit to your interlocuters") doesn't work on this site.
this discussion is filled with people who love high performance computing, and they are mostly saying "I wouldn't buy this" and the reason "too expensive" and they compared it to "business class". my comment was in response to a sharer of that sentiment. (I picked onesuch comment since there is no way to reply to all of them) and pointed out "if you come at this from the other direction, you can see a population of people who will buy it, business class that you consider to be overpriced is full".
>That doesn’t mean the people that buy this laptop fly business class
I didn't say it did.
you should look at the totally of comments and consider "in what way way can this comment be considered correct" rather than looking for ways to say things are incorrect because you didn't anticipate what was going to be said.
The AMD hardware is x86_64, which means lower battery life on average for normal users, and as an architecture, it is very well supported across the Windows ecosystem.
The RTX Spark is ARM64, which means more efficient power management at the cost of a small hit to sustained performance loads. But comes at the cost of being less supported when it comes to available software packages.
Aside from this, I’m led to believe that the AMD stack when it comes to local models has more overhead related to its need to translate more NVIDIA native instructions and therefore can be less performant than Nvidia.
Not sure how true this is anymore, but ive yet to hear otherwise.
> The AMD hardware is x86_64, which means lower battery life on average for normal users [...]
A Ryzen AI Max+ 395 powered tablet with 70Wh [0] gets the same battery life given the same task as a Nvidia N1X powered laptop with 99.9 Wh [1].
Power Consumption in their "Cyberpunk / Stress Test" is also lower at Ø109.3 (108.8-110.8) Watt for the x86_64 vs Ø134.5 (132.1-137.5) Watt for the arm64 system using the same settings, resolution and display brightness. They also get the same performance when normalising for resolutions and settings in Cyberpunk, so this is not an instance of one providing significantly more performance.
To add, battery life on this chip hasn’t been that impressive [1]. It’s basically matching recent MacBook Pros, while sporting a significantly larger battery. Both AMD and Intel have made significant strides in improving power consumption on their latest core designs, to the point where there isn’t a lot of difference anymore.
> Aside from this, I’m led to believe that the AMD stack when it comes to local models has more overhead related to its need to translate more NVIDIA native instructions and therefore can be less performant than Nvidia. Not sure how true this is anymore, but ive yet to hear otherwise.
Uh, well you'd just download lemonade server and use models designed explicitly for the hardware from hugging face.
Coding while flying is often the use case people bring up, but the power brick these laptops come with sometimes trip the plane's power plug in coach and you can't run local AI on battery for long. Also, over time planes will get better connectivity (Starlink etc.) and that particular full-offline scenario will go away.
For gaming, there are better options with dedicated GPUs like the Asus Zephyrus and similar.
What am I missing?