I was in the market for a laptop this month. Many new laptops now advertise AI features like this "HP OmniBook 5 Next Gen AI PC" which advertises:
"SNAPDRAGON X PLUS PROCESSOR - Achieve more everyday with responsive performance for seamless multitasking with AI tools that enhance productivity and connectivity while providing long battery life"
I don't want this garbage on my laptop, especially when its running of its battery! Running AI on your laptop is like playing Starcraft Remastered on the Xbox or Factorio on your steamdeck. I hear you can play DOOM on a pregnancy test too. Sure, you can, but its just going to be a tedious inferior experiance.
Really, this is just a fine example of how overhyped AI is right now.
Laptop manufacturers are too desperate to cash on the AI craze. There's nothing special about an 'AI PC'. It's just a regular PC with Windows Copilot... which is a standard Windows feature anyway.
>I don't want this garbage on my laptop, especially when its running of its battery!
The one bit of good news is it's not going to impact your battery life because it doesn't do any on-device processing. It's just calling an LLM in the cloud.
Doesn't this lead to a lot of tension between the hardware makers and Microsoft?
MS wants everyone to run Copilot on their shiny new data centre, so they can collect the data on the way.
Laptop manufacturers are making laptops that can run an LLM locally, but there's no point in that unless there's a local LLM to run (and Windows won't have that because Copilot). Are they going to be pre-installing Llama on new laptops?
Are we going to see a new power user / normal user split? Where power users buy laptops with LLMs installed, that can run them, and normal folks buy something that can call Copilot?
> MS wants everyone to run Copilot on their shiny new data centre, so they can collect the data on the way.
MS doesn't care where your data is, they're happy to go digging through your C drive to collect/mine whatever they want, assuming you can avoid all the dark patterns they use to push you to save everything on OneDrive anyway and they'll record all your interactions with any other AI using Recall
It isn't just copilot that these laptops come with; manufacturers are already putting their own AI chat apps as well.
For example, the LG gram I recently got came with just such an app named Chat, though the "ai button" on the keyboard (really just right alt or control, I forget which) defaults to copilot.
If there's any tension at all, it's just who gets to be the default app for the "ai button" on the keyboard that I assume almost nobody actually uses.
> It's just a regular PC with Windows Copilot... which is a standard Windows feature anyway.
"AI PC" branded devices get "Copilot+" and additional crap that comes with that due to the NPU. Despite desktops having GPUs with up to 50x more TOPs than the requirement, they don't get all that for some reason https://www.thurrott.com/mobile/copilot-pc/323616/microsoft-...
Even collecting and sending all that data to the cloud is going to drain battery life. I'd really rather my devices only do what I ask them to than have AI running the background all the time trying to be helpful or just silently collecting data.
I feel like there's no point to get a graphics card nowadays. Clearly, graphics cards are optimized for graphics; they just happened to be good for AI but based on the increased significance of AI, I'd be surprised if we don't get more specialized chips and specialized machines just for LLMs. One for LLMs, a different one for stable diffusion.
With graphics processing, you need a lot of bandwidth to get stuff in and out of the graphics card for rendering on a high-resolution screen, lots of pixels, lots of refreshes, lots of bandwidth... With LLMs, a relatively small amount of text goes in and a relatively small amount of text comes out over a reasonably long amount of time. The amount of internal processing is huge relative to the size of input and output. I think NVIDIA and a few other companies already started going down that route.
But probably graphics cards will still be useful for stable diffusion; especially AI-generated videos as the inputs and output bandwidth is much higher.
LLMs are enormously bandwidth hungry. You have to shuffle your 800GB neural network in and out of memory for every token, which can take more time/energy than actually doing the matrix multiplies. GPUs are almost not high bandwidth enough.
But even so, for a single user, the output rate for a very fast LLM would be like 100 tokens per second. With graphics, we're talking like 2 million pixels, 60 times a second; 120 million pixels per second for a standard high res screen. Big difference between 100 tokens vs 120 million pixels.
24 bit pixels gives 16 million possible colors... For tokens, it's probably enough to represent every word of the entire vocabulary of every major national language on earth combined.
> You have to shuffle your 800GB neural network in and out of memory
Do you really though? That seems more like a constraint imposed by graphics cards. A specialized AI chip would just keep the weights and all parameters in memory/hardware right where they are and update them in-situ. It seems a lot more efficient.
I think that it's because graphics cards have such high bandwidth that people decided to use this approach but it seems suboptimal.
But if we want to be optimal; then ideally, only the inputs and outputs would need to move in and out of the chip. This shuffling should be seen as an inefficiency; a tradeoff to get a certain kind of flexibility in the software stack... But you waste a huge amount of CPU cycles moving data between RAM, CPU cache and Graphics card memory.
This doesn't seem right. Where is it shuffling to and from? My drives aren't fast enough to load the model every token that fast, and I don't have enough system memory to unload models to.
If you're using a MoE model like DeepSeek V3 the full model is 671 GB but only 37 GB are active per token, so it's more like running a 37 GB model from the memory bandwidth perspective. If you do a quant of that it could e.g. be more like 18 GB.
I don't doubt that there will be specialized chips that make AI easier, but they'll be more expensive than the graphics cards sold to consumers which means that a lot of companies will just go with graphics cards, either because the extra speed of specialized chips won't be worth the cost, or will they'll be flat out too expensive and priced for the small number of massive spenders who'll shell out insane amounts of money for any/every advantage (whatever they think that means) they can get over everyone else.
Video games have driven the need for hardware more than office work. Sadly games are already being scaled back and more time is being spent on optimization instead of content since consumers can't be expected to have the kind of RAM available they normally would and everyone will be forced to make do with whatever RAM they have for a long time.
That might not be the case. The kind of memory that will flood the second-hand market could not be the kind of memory we can stuff in laptops or even desktop systems.
By "we" do you mean consumers? No, "we" will get neither. This is unexpected, irresistable opportunity to create a new class, by controlling the technology that people are required and are desiring to use (large genAI) with a comprehensive moat — financial, legislative and technological. Why make affordable devices that enable at least partial autonomy? Of course the focus will be on better remote operation (networking, on-device secure computation, advancing narrative that equates local computation with extremism and sociopathy).
This article is so dumb. It totally ignores the memory price explosion that will make large fast memory laptops unfeasible for years and states stuff like this:
> How many TOPS do you need to run state-of-the-art models with hundreds of millions of parameters? No one knows exactly. It’s not possible to run these models on today’s consumer hardware, so real-world tests just can’t be done.
We know exactly the performance needed for a given responsiveness. TOPS is just a measurement independent from the type of hardware it runs on..
The less TOPS the slower the model runs so the user experience suffers. Memory bandwidth and latency plays a huge role too. And context, increase context and the LLM becomes much slower.
We don't need to wait for consumer hardware until we know much much is needed. We can calculate that for given situations.
It also pretends small models are not useful at all.
I think the massive cloud investments will put pressure away from local AI unfortunately. That trend makes local memory expensive and all those cloud billions have to be made back so all the vendors are pushing for their cloud subscriptions. I'm sure some functions will be local but the brunt of it will be cloud, sadly.
I’ve been running LLMs on my laptop (M3 Max 64GB) for a year now and I think they are ready, especially with how good mid sized models are getting. I’m pretty sure unified memory and energy efficient GPUs will be more than just a thing on Apple laptops in the next few years.
Memory prices will rise short term and generally fall long term, even with the current supply hiccup the answer is to just build out more capacity (which will happen if there is healthy competition). I meant, I expect the other mobile chip providers to adopt unified architecture and beefy GPU cores on chip and lots of bandwidth to connect it to memory (at the max or ultra level, at least), I think AMD is already doing UM at least?
> Memory prices will rise short term and generally fall long term, even with the current supply hiccup the answer is to just build out more capacity (which will happen if there is healthy competition)
Don't worry! Sam Altman is on it. Making sure there never is healthy competition that is.
> How many TOPS do you need to run state-of-the-art models with hundreds of millions of parameters? No one knows exactly.
Why not extrapolate from open-source AIs which are available? The most powerful open-source AI (which I know of) is Kimi K2 and >600gb. Running this at acceptable speed requires 600+gb GPU/NPU memory. Even $2000-3000 AI-focused PCs like the DGX spark or Strix Halo typically top out at 128gb. Frontier models will only run on something that costs many times a typical consumer PC, and only going to get worse with RAM pricing.
In 2010 the typical consumer PC had 2-4gb of RAM. Now the typical PC has 12-16gb. This suggests RAM size doubling perhaps every 5 years at best. If that's the case, we're 25-30 years away from the typical PC having enough RAM to run Kimi K2.
But the typical user will never need that much RAM for basic web browsing, etc. The typical computer RAM size is not going to keep growing indefinitely.
What about cheaper models? It may be possible to run a "good enough" model on consumer hardware eventually. But I suspect that for at least 10-15 years, typical consumers (HN readers may not be typical!) will prefer capability, cheapness, and especially reliability (not making mistakes) over being able to run the model locally. (Yes AI datacenters are being subsidized by investors; but they will remain cheaper, even if that ends, due to economies of scale.)
The economics dictate that AI PCs are going to remain a niche product, similar to gaming PCs. Useful AI capability is just too expensive to add to every PC by default. It's like saying flying is so important, everyone should own an airplane. For at least a decade, likely two, it's just not cost-effective.
> It may be possible to run a "good enough" model on consumer hardware eventually
10-15 years?!!!! What is the definition of good enough? Qwen3 8B or A30B are quite capable models which run on a lot of hardware even today. SOTA is not just getting bigger, it's also getting more intelligence and running it more efficiently. There have been massive gains in intelligence at the smaller model sizes. It is just highly task dependent. Arguably some of these models are "good enough" already, and the level of intelligence and instruction following is much better from even 1 year ago. Sure not Opus 4.5 level, but still much could be done without that level of intelligence.
"Good enough" has to mean users won't be frequently frustrated if they transition to it from a frontier model.
> it is highly task dependent... much could be done without that level of intelligence
This is an enthusiast's glass-half-full perspective, but casual end users are gonna have a glass-half-empty perspective. Quen3-8B is impressive, but how many people use it as a daily driver? Most casual users will toss it as soon as it screws up once or twice.
The phrase you quoted in particular was imprecise (sorry) but my argument as a whole still stands. Replace "consumer hardware" with "typical PCs" - think $500 bestseller laptops from Walmart. AI PCs will remain niche luxury products, like gaming PCs. But gaming PCs benefit from being part of gaming culture and because cloud gaming adds input latency. Neither of these affects AI much.
You may be correct, but I wonder if we'll see Mac Mini sized external AI boxes that do have the 1TB of RAM and other hardware for running local models.
Maybe 100% of computer users wouldn't have one, but maybe 10-20% of power users would, including programmers who want to keep their personal code out of the training set, and so on.
I would not be surprised though if some consumer application made it desirable for each individual, or each family, to have local AI compute.
It's interesting to note that everyone owns their own computer, even though a personal computer sits idle half the day, and many personal computers hardly ever run at 80% of their CPU capacity. So the inefficiency of owning a personal AI server may not be as much of a barrier as it would seem.
> In 2010 the typical consumer PC had 2-4gb of RAM. Now the typical PC has 12-16gb. This suggests RAM size doubling perhaps every 5 years at best. If that's the case, we're 25-30 years away from the typical PC having enough RAM to run Kimi K2.
Part of the reason that RAM isn't growing faster is that there's no need for that much RAM at the moment. Technically you can put multiple TB of RAM in your machine, but no-one does that because it's a complete waste of money [0]. Unless you're working in a specialist field 16Gb of RAM is enough, and adding more doesn't make anything noticeably faster.
But given a decent use-case, like running an LLM locally, and you'd find demand for lots more RAM, and that would drive supply, and new technology developments, and in ten years it'll be normal to have 128TB of RAM in a baseline laptop.
Of course, that does require that there is a decent use-case for running an LLM locally, and your point that that is not necessarily true is well-made. I guess we'll find out.
[0] apart from a friend of mine working on crypto who had a desktop Linux box with 4TB of RAM in it.
I'm running GPT-OSS 120B on a MacBook Pro M3 Max w/128 GB. It is pretty good, not great, but better than nothing when the wifi on the plane basically doesn't work.
I've been playing around with my own home-built AI server for a couple months now. It is so much better than using a cloud provider. It is the difference between drag racing in your own car, and renting one from a dealership. You are going to learn far more doing things yourself. Your tools will be much more consistent and you will walk away with a far greater understanding of every process.
A basic last-generation PC with something like a 3060ti (12GB) is more than enough to get started. My current rig pulls less than 500w with two cards (3060+5060). And, given the current temperature outside, the rig helps heat my home. So I am not contributing to global warming, water consumption, or any other datacenter-related environmental evil.
I was in the market for a laptop this month. Many new laptops now advertise AI features like this "HP OmniBook 5 Next Gen AI PC" which advertises:
"SNAPDRAGON X PLUS PROCESSOR - Achieve more everyday with responsive performance for seamless multitasking with AI tools that enhance productivity and connectivity while providing long battery life"
I don't want this garbage on my laptop, especially when its running of its battery! Running AI on your laptop is like playing Starcraft Remastered on the Xbox or Factorio on your steamdeck. I hear you can play DOOM on a pregnancy test too. Sure, you can, but its just going to be a tedious inferior experiance.
Really, this is just a fine example of how overhyped AI is right now.
Laptop manufacturers are too desperate to cash on the AI craze. There's nothing special about an 'AI PC'. It's just a regular PC with Windows Copilot... which is a standard Windows feature anyway.
>I don't want this garbage on my laptop, especially when its running of its battery!
The one bit of good news is it's not going to impact your battery life because it doesn't do any on-device processing. It's just calling an LLM in the cloud.
Doesn't this lead to a lot of tension between the hardware makers and Microsoft?
MS wants everyone to run Copilot on their shiny new data centre, so they can collect the data on the way.
Laptop manufacturers are making laptops that can run an LLM locally, but there's no point in that unless there's a local LLM to run (and Windows won't have that because Copilot). Are they going to be pre-installing Llama on new laptops?
Are we going to see a new power user / normal user split? Where power users buy laptops with LLMs installed, that can run them, and normal folks buy something that can call Copilot?
Any ideas?
> MS wants everyone to run Copilot on their shiny new data centre, so they can collect the data on the way.
MS doesn't care where your data is, they're happy to go digging through your C drive to collect/mine whatever they want, assuming you can avoid all the dark patterns they use to push you to save everything on OneDrive anyway and they'll record all your interactions with any other AI using Recall
I had assumed that they needed the usage to justify the investment in the data centre, but you could be right and they don't care.
It isn't just copilot that these laptops come with; manufacturers are already putting their own AI chat apps as well.
For example, the LG gram I recently got came with just such an app named Chat, though the "ai button" on the keyboard (really just right alt or control, I forget which) defaults to copilot.
If there's any tension at all, it's just who gets to be the default app for the "ai button" on the keyboard that I assume almost nobody actually uses.
Interesting. Yeah, that'll be the argument
> It's just a regular PC with Windows Copilot... which is a standard Windows feature anyway.
"AI PC" branded devices get "Copilot+" and additional crap that comes with that due to the NPU. Despite desktops having GPUs with up to 50x more TOPs than the requirement, they don't get all that for some reason https://www.thurrott.com/mobile/copilot-pc/323616/microsoft-...
Even collecting and sending all that data to the cloud is going to drain battery life. I'd really rather my devices only do what I ask them to than have AI running the background all the time trying to be helpful or just silently collecting data.
Copilot is just ChatGPT as an app.
If you don't use it, it will have no impact on your device. And it's not sending your data to the cloud except for anything you paste into it.
>> I'd really rather my devices only do what I ask them to
Linux hears your cry. You have a choice. Make it.
AI PCs also have NPUs which I guess provide accelerated matmuls, albeit less accelerated than a good discrete GPU.
I feel like there's no point to get a graphics card nowadays. Clearly, graphics cards are optimized for graphics; they just happened to be good for AI but based on the increased significance of AI, I'd be surprised if we don't get more specialized chips and specialized machines just for LLMs. One for LLMs, a different one for stable diffusion.
With graphics processing, you need a lot of bandwidth to get stuff in and out of the graphics card for rendering on a high-resolution screen, lots of pixels, lots of refreshes, lots of bandwidth... With LLMs, a relatively small amount of text goes in and a relatively small amount of text comes out over a reasonably long amount of time. The amount of internal processing is huge relative to the size of input and output. I think NVIDIA and a few other companies already started going down that route.
But probably graphics cards will still be useful for stable diffusion; especially AI-generated videos as the inputs and output bandwidth is much higher.
> Clearly, graphics cards are optimized for graphics; they just happened to be good for AI
I feel like the reverse has been true since after the Pascal era.
LLMs are enormously bandwidth hungry. You have to shuffle your 800GB neural network in and out of memory for every token, which can take more time/energy than actually doing the matrix multiplies. GPUs are almost not high bandwidth enough.
But even so, for a single user, the output rate for a very fast LLM would be like 100 tokens per second. With graphics, we're talking like 2 million pixels, 60 times a second; 120 million pixels per second for a standard high res screen. Big difference between 100 tokens vs 120 million pixels.
24 bit pixels gives 16 million possible colors... For tokens, it's probably enough to represent every word of the entire vocabulary of every major national language on earth combined.
> You have to shuffle your 800GB neural network in and out of memory
Do you really though? That seems more like a constraint imposed by graphics cards. A specialized AI chip would just keep the weights and all parameters in memory/hardware right where they are and update them in-situ. It seems a lot more efficient.
I think that it's because graphics cards have such high bandwidth that people decided to use this approach but it seems suboptimal.
But if we want to be optimal; then ideally, only the inputs and outputs would need to move in and out of the chip. This shuffling should be seen as an inefficiency; a tradeoff to get a certain kind of flexibility in the software stack... But you waste a huge amount of CPU cycles moving data between RAM, CPU cache and Graphics card memory.
This doesn't seem right. Where is it shuffling to and from? My drives aren't fast enough to load the model every token that fast, and I don't have enough system memory to unload models to.
From VRAM to the tensor cores and back. On a modern GPU you can have 1-2tb moving around inside the GPU every second.
This is why they use high bandwidth memory for VRAM.
If you're using a MoE model like DeepSeek V3 the full model is 671 GB but only 37 GB are active per token, so it's more like running a 37 GB model from the memory bandwidth perspective. If you do a quant of that it could e.g. be more like 18 GB.
You're probably not using an 800GB model.
It is right. The shuffling is from CPU memory to GPU memory, and from GPU memory to GPU. If you don’t have enough memory you can’t run the model.
I don't doubt that there will be specialized chips that make AI easier, but they'll be more expensive than the graphics cards sold to consumers which means that a lot of companies will just go with graphics cards, either because the extra speed of specialized chips won't be worth the cost, or will they'll be flat out too expensive and priced for the small number of massive spenders who'll shell out insane amounts of money for any/every advantage (whatever they think that means) they can get over everyone else.
I predict we will see compute-in-flash before we see cheap laptops with 128+ gigs of ram.
We’ve had “compute in flash” for a few years now: https://mythic.ai/product/
I can't tell if this is optimism for compute-in-flash or pessimism with how RAM has been going lately!
Memristors are (IME) missing from the news. They promised to act as both persistent storage and fast RAM.
Yeah especially since what is happening in the memory market
Feast and famine.
In three years we will be swimming in more ram than we know what to do with.
Kind of feel that's already the case today... 4GB I find is still plenty for even business workloads.
Video games have driven the need for hardware more than office work. Sadly games are already being scaled back and more time is being spent on optimization instead of content since consumers can't be expected to have the kind of RAM available they normally would and everyone will be forced to make do with whatever RAM they have for a long time.
That might not be the case. The kind of memory that will flood the second-hand market could not be the kind of memory we can stuff in laptops or even desktop systems.
You could get 128gb ram laptops from the time ddr4 came around: workstation class laptops with 4 ram slots would happily take 128gb of memory.
The fact that nowadays there are little to no laptops with 4 ran slots is entirely artificial.
By "we" do you mean consumers? No, "we" will get neither. This is unexpected, irresistable opportunity to create a new class, by controlling the technology that people are required and are desiring to use (large genAI) with a comprehensive moat — financial, legislative and technological. Why make affordable devices that enable at least partial autonomy? Of course the focus will be on better remote operation (networking, on-device secure computation, advancing narrative that equates local computation with extremism and sociopathy).
This article is so dumb. It totally ignores the memory price explosion that will make large fast memory laptops unfeasible for years and states stuff like this:
> How many TOPS do you need to run state-of-the-art models with hundreds of millions of parameters? No one knows exactly. It’s not possible to run these models on today’s consumer hardware, so real-world tests just can’t be done.
We know exactly the performance needed for a given responsiveness. TOPS is just a measurement independent from the type of hardware it runs on..
The less TOPS the slower the model runs so the user experience suffers. Memory bandwidth and latency plays a huge role too. And context, increase context and the LLM becomes much slower.
We don't need to wait for consumer hardware until we know much much is needed. We can calculate that for given situations.
It also pretends small models are not useful at all.
I think the massive cloud investments will put pressure away from local AI unfortunately. That trend makes local memory expensive and all those cloud billions have to be made back so all the vendors are pushing for their cloud subscriptions. I'm sure some functions will be local but the brunt of it will be cloud, sadly.
also, state of the art models have hundreds of _billions_ of parameters.
It tells you about their ambitions..
I suppose it depends on the model, code was useless. As a lossy copy of an interactive Wikipedia it could be ok not good or great just ok.
Maybe for creative suggestions and editing it’d be ok.
I’ve been running LLMs on my laptop (M3 Max 64GB) for a year now and I think they are ready, especially with how good mid sized models are getting. I’m pretty sure unified memory and energy efficient GPUs will be more than just a thing on Apple laptops in the next few years.
Only because of Apples unified memory architecture. The groundwork is there, we just need memory to be cheaper so we can fit 512+GB now ;)
Memory prices will rise short term and generally fall long term, even with the current supply hiccup the answer is to just build out more capacity (which will happen if there is healthy competition). I meant, I expect the other mobile chip providers to adopt unified architecture and beefy GPU cores on chip and lots of bandwidth to connect it to memory (at the max or ultra level, at least), I think AMD is already doing UM at least?
> Memory prices will rise short term and generally fall long term, even with the current supply hiccup the answer is to just build out more capacity (which will happen if there is healthy competition)
Don't worry! Sam Altman is on it. Making sure there never is healthy competition that is.
https://www.mooreslawisdead.com/post/sam-altman-s-dirty-dram...
Seems like wishful thinking.
> How many TOPS do you need to run state-of-the-art models with hundreds of millions of parameters? No one knows exactly.
Why not extrapolate from open-source AIs which are available? The most powerful open-source AI (which I know of) is Kimi K2 and >600gb. Running this at acceptable speed requires 600+gb GPU/NPU memory. Even $2000-3000 AI-focused PCs like the DGX spark or Strix Halo typically top out at 128gb. Frontier models will only run on something that costs many times a typical consumer PC, and only going to get worse with RAM pricing.
In 2010 the typical consumer PC had 2-4gb of RAM. Now the typical PC has 12-16gb. This suggests RAM size doubling perhaps every 5 years at best. If that's the case, we're 25-30 years away from the typical PC having enough RAM to run Kimi K2.
But the typical user will never need that much RAM for basic web browsing, etc. The typical computer RAM size is not going to keep growing indefinitely.
What about cheaper models? It may be possible to run a "good enough" model on consumer hardware eventually. But I suspect that for at least 10-15 years, typical consumers (HN readers may not be typical!) will prefer capability, cheapness, and especially reliability (not making mistakes) over being able to run the model locally. (Yes AI datacenters are being subsidized by investors; but they will remain cheaper, even if that ends, due to economies of scale.)
The economics dictate that AI PCs are going to remain a niche product, similar to gaming PCs. Useful AI capability is just too expensive to add to every PC by default. It's like saying flying is so important, everyone should own an airplane. For at least a decade, likely two, it's just not cost-effective.
> It may be possible to run a "good enough" model on consumer hardware eventually
10-15 years?!!!! What is the definition of good enough? Qwen3 8B or A30B are quite capable models which run on a lot of hardware even today. SOTA is not just getting bigger, it's also getting more intelligence and running it more efficiently. There have been massive gains in intelligence at the smaller model sizes. It is just highly task dependent. Arguably some of these models are "good enough" already, and the level of intelligence and instruction following is much better from even 1 year ago. Sure not Opus 4.5 level, but still much could be done without that level of intelligence.
"Good enough" has to mean users won't be frequently frustrated if they transition to it from a frontier model.
> it is highly task dependent... much could be done without that level of intelligence
This is an enthusiast's glass-half-full perspective, but casual end users are gonna have a glass-half-empty perspective. Quen3-8B is impressive, but how many people use it as a daily driver? Most casual users will toss it as soon as it screws up once or twice.
The phrase you quoted in particular was imprecise (sorry) but my argument as a whole still stands. Replace "consumer hardware" with "typical PCs" - think $500 bestseller laptops from Walmart. AI PCs will remain niche luxury products, like gaming PCs. But gaming PCs benefit from being part of gaming culture and because cloud gaming adds input latency. Neither of these affects AI much.
You may be correct, but I wonder if we'll see Mac Mini sized external AI boxes that do have the 1TB of RAM and other hardware for running local models.
Maybe 100% of computer users wouldn't have one, but maybe 10-20% of power users would, including programmers who want to keep their personal code out of the training set, and so on.
I would not be surprised though if some consumer application made it desirable for each individual, or each family, to have local AI compute.
It's interesting to note that everyone owns their own computer, even though a personal computer sits idle half the day, and many personal computers hardly ever run at 80% of their CPU capacity. So the inefficiency of owning a personal AI server may not be as much of a barrier as it would seem.
> but I wonder if we'll see Mac Mini sized external AI boxes that do have the 1TB of RAM
Isn't that the Mac Studio already? Ok, it seems to max at 512 GB.
> In 2010 the typical consumer PC had 2-4gb of RAM. Now the typical PC has 12-16gb. This suggests RAM size doubling perhaps every 5 years at best. If that's the case, we're 25-30 years away from the typical PC having enough RAM to run Kimi K2.
Part of the reason that RAM isn't growing faster is that there's no need for that much RAM at the moment. Technically you can put multiple TB of RAM in your machine, but no-one does that because it's a complete waste of money [0]. Unless you're working in a specialist field 16Gb of RAM is enough, and adding more doesn't make anything noticeably faster.
But given a decent use-case, like running an LLM locally, and you'd find demand for lots more RAM, and that would drive supply, and new technology developments, and in ten years it'll be normal to have 128TB of RAM in a baseline laptop.
Of course, that does require that there is a decent use-case for running an LLM locally, and your point that that is not necessarily true is well-made. I guess we'll find out.
[0] apart from a friend of mine working on crypto who had a desktop Linux box with 4TB of RAM in it.
I spent a good 30 seconds trying to figure out what DDS was an acronym for in this context.
I'm running GPT-OSS 120B on a MacBook Pro M3 Max w/128 GB. It is pretty good, not great, but better than nothing when the wifi on the plane basically doesn't work.
I have no desire to run an LLM on my laptop when I can run one on a computer the size of six football fields.
I've been playing around with my own home-built AI server for a couple months now. It is so much better than using a cloud provider. It is the difference between drag racing in your own car, and renting one from a dealership. You are going to learn far more doing things yourself. Your tools will be much more consistent and you will walk away with a far greater understanding of every process.
A basic last-generation PC with something like a 3060ti (12GB) is more than enough to get started. My current rig pulls less than 500w with two cards (3060+5060). And, given the current temperature outside, the rig helps heat my home. So I am not contributing to global warming, water consumption, or any other datacenter-related environmental evil.
This must be referring mostly to windows, or non-Apple laptops