[HN Gopher] Microsoft CTO says he wants to swap most AMD and Nvi...
___________________________________________________________________
Microsoft CTO says he wants to swap most AMD and Nvidia GPUs for
homemade chips
Author : fork-bomber
Score : 168 points
Date : 2025-10-03 14:48 UTC (8 hours ago)
(HTM) web link (www.cnbc.com)
(TXT) w3m dump (www.cnbc.com)
| okokwhatever wrote:
| somebody wants to buy NVDA cheaper... ;)
| synergy20 wrote:
| plus someone has no leverage whatsoever other than talking
| ambicapter wrote:
| Microsoft, famously resource-poor.
| fidotron wrote:
| They have so much money it is harmful to their ability to
| execute.
|
| Just look at the implosion of the XBox business.
| fennecbutt wrote:
| Granted, if everyone had done what the highly paid
| executives had told them to do, xbox would never have
| existed.
|
| And I'm guessing that the decline is due to executive
| meddling.
|
| What is it that executives do again? Beyond collecting
| many millions of dollars a year, that is.
| fidotron wrote:
| They sit around fantasizing about buying Nintendo because
| that would be the top thing they could achieve in their
| careers.
| rjbwork wrote:
| Guess MSFT needs somewhere else AI adjacent to funnel money into
| to produce the illusion of growth and future cash flow in this
| bubblified environment.
| balls187 wrote:
| > produce the illusion of growth and future cash flow in this
| bubblified environment.
|
| I was ranting about this to my friends; Wallstreet is now
| banking on Tech firms to produce the illusion of growth and
| returns, rather than repackaging and selling subprime
| mortgages.
|
| The tech sector seems to have a never ending supply of things
| to spur investment and growth: cloud computing, saas, mobile,
| social media, IoT, crypto, Metaverse, and now AI.
|
| Some useful, some not so much.
|
| Tech firms have a lot of pressure to produce growth, it's
| filled with very smart people, and wields influence on public
| policy. The flip side is the mortage crisis, at least before it
| collapsed, got more Americans into home ownership (even if they
| weren't ready for it). I'm not sure the tech sectors meteoric
| rise has been as helpful (sentiment of locals in US tech hubs
| suggests a overall feeling of dissatisfaction with tech)
| giancarlostoro wrote:
| So similar to Apple Silicon. If this means they'll be on par with
| Apple Silicon I'm okay with this, I'm surprised they didn't do
| this sooner for their Surface devices.
|
| Oh right, for their data centers. I could see this being useful
| there too, brings costs down lower.
| CharlesW wrote:
| > _So similar to Apple Silicon._
|
| Yes, in the sense that this is at least partially inspired by
| Apple's vertical integration playbook, which has now been
| extended to their own data centers based on custom Apple
| Silicon1 and a built-for-purpose, hardened edition of Darwin2.
|
| 1 https://security.apple.com/blog/private-cloud-compute/ 2
| https://en.wikipedia.org/wiki/Darwin_(operating_system)
| giancarlostoro wrote:
| Yeah, its interesting, years ago I never thought Apple nor
| Microsoft would do this, but also Google has done this on
| their cloud as well, so it makes sense.
| georgeburdell wrote:
| Vertical integration only works if your internal teams can
| stay in the race at each level well enough to keep the stack
| competitive as a whole. Microsoft can't attract the same
| level of talent as Apple because their pay is close to the
| industry median
| quadrature wrote:
| Not suprising that the hyperscalers will make this decision for
| inference and maybe even a large chunk of training. I wonder if
| it will spur nvidia to work on an inference only accelerator.
| edude03 wrote:
| > I wonder if it will spur nvidia to work on an inference only
| accelerator.
|
| Arguably that's a GPU? Other than (currently) exotic ways to
| run LLMs like photonics or giant SRAM tiles there isn't a
| device that's better at inference than GPUs and they have the
| benefit that they can be used for training as well. You need
| the same amount of memory and the same ability to do math as
| fast as possible whether its inference or training.
| CharlesW wrote:
| > _Arguably that 's a GPU?_
|
| Yes, and to _@quadrature_ 's point, NVIDIA _is_ creating GPUs
| explicitly focused on inference, like the Rubin CPX:
| https://www.tomshardware.com/pc-components/gpus/nvidias-
| new-...
|
| _" ...the company announced its approach to solving that
| problem with its Rubin CPX-- Content Phase aXcelerator --
| that will sit next to Rubin GPUs and Vera CPUs to accelerate
| specific workloads."_
| edude03 wrote:
| Yeah, I'm probably splitting hairs here but as far as I
| understand (and honestly maybe I don't understand) - Rubin
| CPX is "just" a normal GPU with GDDR instead of HBM.
|
| In fact - I'd say we're looking at this backwards - GPUs
| used to be the thing that did math fast and put the result
| into a buffer where something else could draw it to a
| screen. Now a "GPU" is still a thing that does math fast,
| but now sometimes, you don't include the hardware to put
| the pixels on a screen.
|
| So maybe - CPX is "just" a GPU but with more generic naming
| that aligns with its use cases.
| bonestamp2 wrote:
| There are some inference chips that are fundamentally
| different from GPUs. For example, one of the guys who
| designed Google's original TPU left and started a company
| (with some other engineers) called groq ai (not to be
| confused with grok ai). They make a chip that is quite
| different from a GPU and provides several advantages for
| inference over traditional GPUs:
|
| https://www.cdotrends.com/story/3823/groq-ai-chip-
| delivers-b...
| AzN1337c0d3r wrote:
| I would submit Google's TPUs are not GPUs.
|
| Similarly, Tenstorrent seems to be building something that
| you could consider "better", at least insofar that the goal
| is to be open.
| quadrature wrote:
| I'm not very well versed, but i believe that training
| requires more memory to store intermediate computations so
| that you can calculate gradients for each layer.
| conradev wrote:
| They're already optimizing GPU die area for LLM inference
| over other pursuits: the FP64 units in the latest Blackwell
| GPUs were greatly reduced and FP4 was added
| imtringued wrote:
| The AMD NPU has more than 2x the performance per watt versus
| basically any Nvidia GPU. Nvidia isn't leading because they
| are power efficient.
|
| And no, the NPU isn't a GPU.
| nsteel wrote:
| Isn't Etched's Soho ASIC claimed to be much better than a
| GPU?
|
| https://www.etched.com/announcing-etched
| hkt wrote:
| Even just saying this applies downward pressure on pricing:
| NVIDIA has an enormous amount of market power (~"excess" profit)
| right now and there aren't enough near competitors to drive that
| down. The only thing that will work is their biggest _consumers_
| investing, or threatening to invest, if their prices are too
| high.
|
| Long term, I wonder if we're exiting the "platform compute" era,
| for want of a better term. By that I mean compute which can run
| more or less any operating system, software, etc. If everyone is
| siloed into their own vertically integrated hardware+operating
| system stack, the results will be awful for free software.
| startupsfail wrote:
| In that case, it's great that Microsoft is building their
| silicon. Keeps NVIDIA in check, otherwise these profits would
| evaporate into nonsense and NVIDIA would lose the AI industry
| to competition from China. Which, depending if AGI/ASI is
| possible or not, may or may not be a great move.
| harrall wrote:
| Google has been using its own TPU silicon for machine learning
| since 2015.
|
| I think they do all deep learning for Gemini on ther own silicon.
|
| But they also invented AI as we know it when they introduced
| transformer architecture and they've been more invested in
| machine learning than most companies for a very long time.
| chrismustcode wrote:
| I thought they use GPU for learning and TPU for inference, I'm
| open to been corrected.
| cendyne wrote:
| I've heard its a mixture because they can't source enough in-
| house compute
| xnx wrote:
| Some details here:
| https://news.ycombinator.com/item?id=42392310
| surajrmal wrote:
| The first tpu they made was inference only. Everything since
| has been used for training. I think that means they weren't
| using it for training in 2015 but rather 2017 based on
| Wikipedia.
| lokar wrote:
| The first TPU they *announced" was for inference
| dekhn wrote:
| no. for internal training most work is done on TPUs, which
| have been explicitly designed for high performance training.
| buildbot wrote:
| Not that it matters, but Microsoft has been doing AI
| accelerators for a bit too - project Brainwave has been around
| since 2018 - https://blogs.microsoft.com/ai/build-2018-project-
| brainwave/
| interestpiqued wrote:
| Yeah I worked in the hardware org around this time. We got
| moved from under Xbox org to azure and our main work became
| AI related accelerators
| buildbot wrote:
| Very cool! Catapult/Brainwave is what got me into hardware
| & ML stuff :)
| rjzzleep wrote:
| The first revisions were stuff made by qualcomm right? I don't
| think we have much data on how much customizations they make
| and where they their IP from, but given how much of the Tensor
| cores comes from Samsung I think it's safe to say to assume
| that there is a decent amount coming from some of the big
| vendors.
| Moto7451 wrote:
| To be fair that's a pretty good approach if you look at
| Apple's progression from assembled IPs in the first iPhone
| CPU to the A and M series.
| rjzzleep wrote:
| Yeah but at least when it comes to mobile CPUs Apple seemed
| vastly more competent in how they approached it.
| osivertsson wrote:
| Apple generally tries to erase info about acquisitions from
| their official company story, they want it to look like
| internal Apple innovation.
|
| When it comes to CPUs they bought P.A. Semi back in 2008
| and got a lot of smart people with decades of relevant
| experience that were doing cutting-edge stuff at the time.
|
| This was immensely important to be able to deliver current
| Apple CPUs.
| foobiekr wrote:
| PA Semi was kind of an interesting acquisition. The team
| was definitely very skilled, but there are always
| gotchas. Before the acquisition happened we were on their
| receiving end of their dual core PPC and it was not great
| at all. We had a lot of issues with board bringup, power,
| and heat. More errata than I've ever seen. We eventually
| had to went with x86 for the project instead, which was
| more performant and certainly a lot easier overall at the
| time.
|
| I had previously encountered some of that team with the
| SiByte MIPS in an embedded context, I know they were
| highly skilled, they had tons of pedigree, but PA Semi
| itself was a strange beast.
| conradev wrote:
| For TPUs I believe it is Broadcom:
| https://www.theregister.com/2023/09/22/google_broadcom_tpus/
|
| Not sure about the mobile SoCs
| foobiekr wrote:
| Broadcom fills the same role for Google TPU that Marvell
| fills for Trainium @ amazon.
| kccqzy wrote:
| It's "made by" TSMC as usual. Their customization comes from
| identifying which compute operations that want optimized in
| hardware and do it themselves. And then they buy non-compute
| IP like HBM from Broadcom. And Broadcom also does things like
| physical design.
| Der_Einzige wrote:
| I'm 99.999% sure that the claim of "all deep learning for
| Gemini on their own silicon" is not true.
|
| Maybe if you restrict it similarly to the Deepseek paper to
| "Gemini uses TPU for the final successful training run and for
| scaled inference" you might be correct, but there's no way that
| GPUs aren't involved for at minimum comparability and more
| rapid iteration reasons during the extremely buggy and error
| prone point of getting to the final training run. Certainly the
| theoretical and algorithmic innovations that are often being
| done at Google and do make their way into Gemini also sometimes
| using Nvidia GPUs.
|
| GCP has a lot of, likely on the order of at least 1 million
| GPUs in their fleet today (I'm likely underestimating). Some of
| that is used internally and is made available to their
| engineering staff. What constitutes "deep learning for gemini"
| is very up to interpretation.
| imtringued wrote:
| Google is the creator of JAX and XLA. Maybe the developer
| laptops have Nvidia GPUs and they do some testing there, but
| for Google there is literally no point in bothering with
| CUDA, pytorch or any other ecosystem strongly focused on
| Nvidia GPUs.
|
| In my experience JAX is way more flexible than pytorch the
| moment you want to do things that aren't training ML models.
| E.g. you want to build an optimizer that uses the derivative
| of your model with respect to the input.
| kingstnap wrote:
| Honestly Pytorch is weird imo, I'm surprised people love it
| so much.
|
| Loss.backward? Tensor.grad? Optimizer.zero grad()? With
| torch.no_grad()?
|
| What is with all these objects holding pointers to stuff?
| An ndarray is a pointer to memory and a shape my dudes. A
| gradient is the change in a scalar function w.r.t to some
| inputs.
| kccqzy wrote:
| You seem to think GPUs are better than TPUs for rapid
| iteration. Why is that? There's no inherent reason why one is
| more suited to rapid iteration than another; it's entirely a
| matter of developer tooling and infrastructure. And Google
| famously has excellent tooling. And furthermore, the tooling
| Google exposes to the outside world is usually poorer than
| the tooling used internally by Googlers.
| bjourne wrote:
| It was a few years ago since I last played with Googles hw,
| but iirc TPUs were inflexible and very fast. Worked well
| for linear and convolutional layers but could not
| accelerate certain LSTM configurations. For such networks
| GPUs were faster. It wouldn't surprise me the least if TPU
| hardware support lagged behind what the latest and greatest
| LLMs require for training.
| summerlight wrote:
| Internally, TPU is much cheaper for the same amount of
| compute compared to GPU, so I don't see much reasons why they
| need to use GPU. Probably >99% of compute budgets are spent
| on TPU. It might be true if you say these <1% still counts,
| but I guess it is pretty safe to say all of its meaningful
| production workload are running on TPU. It is simply too
| expensive to run a meaningful amount of compute on non-TPU.
|
| Just to clarify, TPU has been in development for a decade and
| it is quite mature these days. Years ago internal consumers
| had to accept the CPU/GPU and TPU duality but I think this
| case is getting rarer. I guess this is even more true for
| DeepMind since itself owns a ML infra team. They likely be
| able to fix most of the issues with a high priority.
| owkman wrote:
| That's a strange position to take with such high certainty.
| Google has been talking about training on TPUs for a long
| time. Many ex and current employees have been on the record
| talking about how much nicer the Google internal training
| infra using TPUs is. GPU is an afterthought for Google's
| inference and non-existent in training.
| leshokunin wrote:
| Honestly this would be great for competition. Would love to see
| them impish in that direction.
| bee_rider wrote:
| The most important note is:
|
| > The software titan is rather late to the custom silicon party.
| While Amazon and Google have been building custom CPUs and AI
| accelerators for years, Microsoft only revealed its Maia AI
| accelerators in late 2023.
|
| They are too late for now, they realistically hardware takes a
| couple generations to become a serious contender and by the time
| Microsoft has a chance to learn from their hardware mistakes the
| "AI" bubble will have popped.
|
| But, there will probably be some little LLM tools that do end up
| having practical value; maybe there will be a happy line-crossing
| point for MS and they'll have cheap in-house compute when the
| models actually need to be able to turn a profit.
| surajrmal wrote:
| At this point it will take a lot of investment to catch up.
| Google relies heavily on specialized interconnects to build
| massive tpu clusters. It's more than just designing a chip
| these days. Folks who work on interconnects are a lot more rare
| than engineers who can design chips.
| alephnerd wrote:
| > hardware takes a couple generations to become a serious
| contender
|
| Not really and for the same reason Chinese players like Biren
| are leapfrogging - much of the workload profile in AI/ML is
| "embarrassingly parallel", thus reducing the need for
| individual ASICs to be bleeding edge performant.
|
| If you are able to negotiate competitive fabrication and energy
| supply deals, you can mass produce your way into providing
| "good enough" performance.
|
| Finally, the persona who cares about hardware performance in
| training isn't in the market for cloud offered services.
| kenjackson wrote:
| And current LLM architectures affinitize differently to HW
| than DNNs even just a decade ago. If you have the money and
| technical expertise (both of which I assume MS has access to)
| then a late start might actually be beneficial.
| rcxdude wrote:
| As I understood it the main bottleneck is interconnects,
| anyhow. It's more difficult to keep the ALUs fed than it is
| to make them fast enough, especially once your model can't
| fit in one die/PCB. And that's in principle a much trickier
| part of the design, so I don't really know how that shakes
| out (is there a good enough design that you can just buy as a
| block?)
| alephnerd wrote:
| The bet I'm seeing is to try and invest in custom ASICs to
| become integrated as part of an SoC to solve that
| interconnect bottleneck.
|
| It's largely a solved problem based on Google/Broadcom's
| TPU work - almost everyone is working with Broadcom to
| design their own custom ASIC and SoC.
| interestpiqued wrote:
| Most of the big players started working on hardware for this
| stuff in 2018/2019. I worked at MSFT silicon org during this
| time. Meta was also hiring my coworkers for similar projects. I
| left a few years ago and don't know current state but they
| already have some generations under their belt
| cjbgkagh wrote:
| I guess Microsoft's investment into Graphcore didn't pay off. Not
| sure what they're planning but more of that isn't going to cut
| it. At the time (late 2019) I was arguing for either a GPU
| approach or specialized architecture targeting transformers.
|
| There was a split at MS where the 'Next Gen' bayesian was being
| done in the US and the frequentist work was being shipped off to
| China. Chris Bishop was promoted to head of MSR Cambridge which
| didn't help.
|
| Microsoft really is an institutionally stupid organization so I
| have no idea on which direction they actually go. My best guess
| is that it's all talk.
| Den_VR wrote:
| Microsoft lacks the credibility and track record for this to be
| anything but talk. Hardware doesn't simply go from zero to
| gigawatts of infrastructure on talk. Even Apple is better
| positioned for such a thing.
| pjmlp wrote:
| Microsoft has plenty of home grown hardware on Azure, some of
| which even has firmware written in Rust nowadays.
| buildbot wrote:
| Yep, tons of decent homegrown stuff in Azure. And for a
| long time - see the Catapult FPGA cards.
| migueldeicaza wrote:
| They do have such a dedicated chip, the MAIA 100 chip which is
| an in-house chip, and it is a chip that was designed in the era
| of transformers, and this is what is being discussed in the
| interview.
| cjbgkagh wrote:
| I missed that, it's been a few years since I've paid
| attention to MS hardware and it is very possible that my
| thoughts are out of date. I left MS with a rather bad taste
| in my mouth. I'm checking out the info on that chip and what
| I am seeing is a little light on details. Just TPUs and fast
| interconnects.
|
| What I've found; MIAI 200 the next version is having issues
| due to brain drain, and MIAI 300 is to be an entirely new
| architecture so the status for that is rather uncertain.
|
| I think a big reason MS invested so heavily into OpenAI was
| to have a marquee customer push cultural change through the
| org, which was a necessary decision. If that eventually
| yields in a useful chip I will be impressed, I hope it does.
| latchkey wrote:
| It always falls back on the software. AMD is behind, not because
| the hardware is bad, but because their software historically has
| played second fiddle to their hardware. The CUDA moat is real.
|
| So, unless they also solve that issue with their own hardware,
| then it will be like the TPU, which is limited to usage primarily
| at Google, or within very specific use cases.
|
| There are only so many super talented software engineers to go
| around. If you're going to become an expert in something, you're
| going to pick what everyone else is using first.
| amelius wrote:
| > The CUDA moat is real.
|
| I don't know. The transformer architecture uses only a limited
| number of primitives. Once you have ported those to your new
| architecture, you're good to go.
|
| Also, Google has been using TPUs for a long time now, and
| __they__ never hit a brick wall for a lack of CUDA.
| latchkey wrote:
| It is beyond porting, it is mentality of developers. Change
| is expensive and I'm not just talking about $ value.
|
| > Also, Google has been using TPUs for a long time now, and
| __they__ never hit a brick wall for a lack of CUDA.
|
| That's exactly what I'm saying. __they__ is the keyword.
| amelius wrote:
| Not sure what you mean. Google is a big company. Their TPUs
| have many users internally.
| latchkey wrote:
| Very few developers outside of Google have ever written
| code for a TPU. In a similar way, far fewer have written
| code for AMD, compared to NVIDIA.
|
| If you're going to design a custom chip and deploy it in
| your data centers, you're also committing to hiring and
| training developers to build for it.
|
| That's a kind of moat, but with private chips. While you
| solve one problem (getting the compute you want), you
| create another: supporting and maintaining that ecosystem
| long term.
|
| NVIDIA was successful because they got their hardware
| into developers hands, which created a feedback loop,
| developers asked for fixes/features, NVIDIA built them,
| the software stack improved, and the hardware evolved
| alongside it. That developer flywheel is what made CUDA
| dominant and is extremely hard to replicate because the
| shortage of talented developers is real.
| amelius wrote:
| I mean it's all true to some extent. But that doesn't
| mean implementing the few primitives to get transformers
| running requires CUDA, or that it's an impossible task.
| Remember, we're talking about >$1B companies here who can
| easily assemble teams of 10s-100s of developers.
|
| You can compare CUDA to the first PC OS, DOS 1.0. Sure,
| DOS was viewed as a moat at the time, but it didn't keep
| others from kicking its ass.
| latchkey wrote:
| > _You can compare CUDA to the first PC OS, DOS 1.0._
|
| Sorry, I don't understand this comparison at all. CUDA
| isn't some first version of an OS, not even close. It's
| been developed for almost 20 years now. Bucketloads of
| documentation, software and utility have been created
| around it. It won't have its ass kicked by any stretch of
| imagination.
| amelius wrote:
| Yes, CUDA has a history. And it shows. CUDA has very bad
| integration with the OS for example. It's time some other
| company (Microsoft sounds like a good contender) showed
| them how you do this the right way.
|
| Anyway, this all distracts from the fact that you don't
| need an entire "OS" just to run some arithmetic
| primitives to get transformers running.
| latchkey wrote:
| > _CUDA has very bad integration with the OS for
| example._
|
| If you want to cherry pick anything, you can. But in my
| eyes, you're just solidifying my point. Software is
| critical. Minimizing the surface is obviously a good
| thing (tinygrad for example), but you're still going to
| need people who are willing and able to write the code.
| amelius wrote:
| OK, but Microsoft is a software company ...
| VirusNewbie wrote:
| >Very few developers outside of Google have ever written
| code for a TPU.
|
| Anthropic?
| imtringued wrote:
| Internal ASICs are a completely different market. You know your
| workloads and there is a finite number of them. It's as if you
| had to build a web browser, normally an impossible task, except
| it only needs to work with your company website, which only
| uses 1% of all of the features a browser offers.
| latchkey wrote:
| Very true indeed. I'm not arguing against that at all.
| migueldeicaza wrote:
| The CUDA moat is real for general purpose computing and for
| researchers that want a swiss army knife, but when it comes to
| well known deployments, for either training or inference, the
| amount of stuff that you need from a chip is quite limited.
|
| You do not need most of CUDA, or most of the GPU functionality,
| so dedicated chips make sense. It was great to see this theory
| put to the test in the original llama.cpp stack which showed
| just what you needed, the tiny llama.c that really shows how
| little was actually needed and more recently how a small team
| of engineers at Apple put together MLX.
| latchkey wrote:
| Absolutely agreed on the need for just specific parts of the
| chip and tailoring to that. My point is bigger than that.
| Even if you build a specific chip, you still need engineers
| who understand the full picture.
| outside1234 wrote:
| For GPUs at least this is pretty obvious. For CPUs it is less
| clear to me that they can do it more efficiently.
| sgerenser wrote:
| When Microsoft talks about "making their own CPUs," they just
| mean putting together a large number of off-the-shelf Arm
| Neoverse cores into their own SoC, not designing a fully custom
| CPU. This is the same thing that Google and Amazon are doing as
| well.
| qwertytyyuu wrote:
| better late than never to get into to game... right? right....?
| johncolanduoni wrote:
| Just like mobile!
| alephnerd wrote:
| I've mentioned this before on HN [0][1].
|
| The name of the game has been custom SoCs and ASICs for a couple
| years now, because inference and model training is an
| "embarrassingly parallel" problem, and models that are optimized
| for older hardware can provide similar gains to models that are
| run on unoptimized but more performant hardware.
|
| Same reason H100s remain a mainstay in the industry today, as
| their performance profile is well understood now.
|
| [0] - https://news.ycombinator.com/item?id=45275413
|
| [1] - https://news.ycombinator.com/item?id=43383418
| philipwhiuk wrote:
| > The name of the game has been custom SoCs and ASICs for a
| couple years now, because inference and model training is an
| "embarrassingly parallel" problem, and models that are
| optimized for older hardware can provide similar gains to
| models that are run on unoptimized but more performant
| hardware.
|
| Is anyone else getting crypto flashbacks?
| kcb wrote:
| The difference is crypto wasn't memory and throughput
| dependent. That's why a small asic on a USB stick could
| outperform a GPU.
| alephnerd wrote:
| One thing to point out - "ASIC" is more of a business term
| than a technical term.
|
| The teams that work on custom ASIC design at (eg.) Broadcom
| for Microsoft are basically designing custom GPUs for MS,
| but these will only meet the requirements that Microsoft
| lays out, and Microsoft would have full insight and
| visibility into the entire architecture.
| croisillon wrote:
| homemade chips is probably a lot of fun but buying regular Lay's
| is so much easier
| babuloseo wrote:
| The current M$ sure is doing a great job at making people move to
| alternatives.
| bongodongobob wrote:
| People, sure, but that's not their target demographic. It's
| businesses and they aren't moving away from MS anytime soon.
| floxy wrote:
| On a slightly different tangent, is anyone working on analog
| machine learning ASICs? Sub-threshold CMOS or something? I mean
| even at the research level? Using a handful of transistor for an
| analog multiplier. And get all of the crazy fascinating
| translinear stuff of Barrie Gilbert fame.
|
| https://www.electronicdesign.com/technologies/analog/article...
|
| https://www.analog.com/en/resources/analog-dialogue/articles...
|
| http://madvlsi.olin.edu/bminch/talks/090402_atact.pdf
| nickpsecurity wrote:
| A bunch of people. Just type these terms into DuckDuckGo:
|
| analog neural network hardware
|
| physical neural network hardware
|
| Put "this paper" after each one to get academic research. Try
| it with and without that phrase. Also, add "survey" to the next
| iteration.
|
| The papers that pop up will have the internal jargon the
| researchers use to describe their work. You can further search
| with it.
|
| The "this paper," "survey," and internal jargon in various
| combinations are how I find most CompSci things I share.
| accrual wrote:
| Thanks for these helpful search terms!
| tovej wrote:
| Are you aware of dblp.org?
| xadhominemx wrote:
| For large models, the bottlenecks are memory bandwidth,
| network, and power consumption by the DAC/ADC arrays
|
| It's never come even close to penciling out in practice.
|
| For small models there are people working on this implemented
| in flash memory eg Mythic.
| improgrammer007 wrote:
| For those who don't know Msft is working on
| https://azure.microsoft.com/en-us/blog/azure-maia-for-the-er...
| hulitu wrote:
| > For those who don't know Msft is working on
|
| We do know: ads, spyware and rounding corners of UI elements.
|
| If their processor work like their software, i really feel pity
| for people who use it.
| snowwrestler wrote:
| Made where? Isn't foundry capacity the limiting factor on chips
| for AI right now?
| Jyaif wrote:
| By cutting the middle man out, MS could pay TSMC more than
| nvidia per wafer and still save money.
| fishmicrowaver wrote:
| This is the whole game right here.
| nsteel wrote:
| They can even pay Broadcom to be a lower-level middle man
| instead. Despite the BRCM tax, it'll still be way cheaper
| than going to Nvidia.
| migueldeicaza wrote:
| TSMC manufactures the MAIA 100:
|
| https://azure.microsoft.com/en-us/blog/azure-maia-for-the-er...
| blibble wrote:
| well yeah, I can't imagine sending all your shareholders money to
| nvidia to produce slop no-one is willing to pay for is going down
| too well
| sleepybrett wrote:
| Microsoft just can't stop following apple's lead.
| pjmlp wrote:
| Doesn't come as a surprise, I imagine they would also build on
| top of Direct Compute, or something else they can think of.
| alecco wrote:
| For many years, every few months Microsoft and Meta say they are
| going to do AI hardware. But nothing tangible is delivered.
| Herring wrote:
| Yeah they could just be negotiating a better deal w nvidia.
| jtfrench wrote:
| "Microsoft Silicon", coming up.
|
| Is it practical for them to buy an existing chip maker? Or would
| they just go home-grown?
|
| - As of today, Nvidia's market cap is a whopping 4.51 trillion
| USD compared to Microsoft's 3.85 trillion USD, so that might not
| work.
|
| - AMD's market cap is 266.49 billion USD, which is more in reach.
| floxy wrote:
| Intel is almost a bargain at $175 billion
| j_walter wrote:
| They were more of a bargain 3 months ago at $80B.
| hulitu wrote:
| > "Microsoft Silicon", coming up.
|
| Will they equip the new Microsoft Vacuum Cleaner with it ? /s
| arisAlexis wrote:
| They could buy with peanut money Cerebras
| foobarbecue wrote:
| Weird use of "homemade"! I guess they mean "in-house"?
| ravenstine wrote:
| Just like how mama used to make 'em!
| brnt wrote:
| Moms secret ingredient to her AI was Nvidia!
| smallmancontrov wrote:
| It's a delightful coincidence of history that the "What's
| Jensen been cooking" pandemic gag happened on the generation
| that would wake up the AIs.
|
| https://www.youtube.com/watch?v=So7TNRhIYJ8
| john01dav wrote:
| I don't like this trend where all the big tech companies are
| bringing hardware in-house, because it makes it unavailable to
| everyone else. I'd rather not have it be the case that everyone
| who isn't big tech either pays the Nvidia tax or deals with
| comparatively worse hardware, while each big tech has their own.
| If these big tech companies also sold their in-house chips, then
| that would address this problem. I like what Ampere is doing in
| this space.
| Etheryte wrote:
| Another way to look at it would be that if all the big players
| stop buying up all the GPUs, prices will come back down for
| regular consumers, making it available for everyone else.
| jlarocco wrote:
| But there's a real risk that in the long term all of the
| "serious" AI hardware research will get done inside a few big
| companies, essentially shutting out smaller players with a
| hardware moat.
|
| Unless they start selling the hardware, but in the current AI
| market nobody would do that because it's their special sauce.
|
| On the other hand, maybe it's no different than any other
| hardware, and other makers will catch up eventually.
| tyre wrote:
| And some of those people will leave to start their own
| companies or join competitor chipmakers. Innovation will
| continue.
| dang wrote:
| Url changed from
| https://www.theregister.com/2025/10/02/microsoft_maia_dc/, which
| points to this.
|
| Submitters: " _Please submit the original source. If a post
| reports on something found on another site, submit the latter._ "
| - https://news.ycombinator.com/newsguidelines.html
| eptcyka wrote:
| I do too.
| yalogin wrote:
| The big data center owners will want to build their own hardware,
| that is a no brainer for them.
___________________________________________________________________
(page generated 2025-10-03 23:01 UTC)