Tesla’s Dojo Microarchitecture
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Having tons of small CPUs with fast local SRAM is of course not a new idea. Back in 1998, I talked to a startup that believed it could replace standard cell ASIC design with tiny CPUs that had custom instruction sets. (I didn't believe it could: it's extremely area inefficient and way to power hungry for that kind of application. The startup went nowhere.) And the IBM Cell is indeed an obvious inspiration.
But AFAIK, the IBM Cell was hard to program. I've seen PS3 presentations where it was primarily used as a software defined GPU, because it was just too difficult to use as a general purpose processor.
Now NOT being a general purpose process is the whole point of Dojo, so maybe they can make it work. But from my limited experience with CUDA, virtual memory and direct access to DRAM is a major plus, even if the high performance compute routines make intensive use of shared memory. The fact that an interface processor is involved (how?) in managing your local SRAM must make synchronization much more complex than with CUDA, where everything is handled by the same SM that manages the calculations: your warp issues a load, it waits on a barrier, the calculations happens, sometimes in a side unit in which case you again wait on a barrier, you offload the data and wait on a barrier. And while one warp waits on a barrier, another warp can take over. It's pretty straightforward.
The Dojo model suggests that "wait on a barrier" becomes "wait on the interface processor".
Another oddball architecture that went nowhere.
It feels like "within the time limits" has always been the problem of difficult to program for software-dependant architectures: time vs competitors.
E.g. in the time it takes to write an intelligent compiler (IA-64), your better-resourced (because they're getting revenue from the current market) competitor has surpassed your performance via brute evolution force.
There are use cases out there (early supercomputing, NVIDIA) where radical, development-heavy architectures have been successful, but they generally lack of competitor (the former) or iterate ruthlessly themselves (the latter).
Connection machine = only had one customer afaik (NSA)
Transmeta - interesting technology but nobody in that market wanted to run anything besides Windows+x86.
You don't seem to understand the timelines of designing chips like that from scratch.
Dojo has been in works for almost 7 years. Tesla will continue to work on this for the next 20 years. There will be Dojo v2, Dojo v3 etc. just like at SpaceX there was Falcon 1, Falcon 9, Falcon Heavy.
This still might end up a failure but they clearly feel confident enough to talk about Dojo publicly, which wasn't the case for the first 5 years of its development.
There is no doubt that it is an amazing piece of tech but I’m not confident Tesla will be able to pull off beating NVidia. Especially when compared to NVidias Tensor Cores and economics of scale. I don’t like their whole approach to self driving ML. I know a lot of people disagree with me so I would rather not get into it.
I think a lot of the talk of future performance is posturing in order to get NVidia chips at cheaper prices.
I think with better ML they wouldn’t need so many chips, be it NVidia or Dojo.
But your comment is neither here not there.
What do you think is a timeline for designing a chip like Dojo from scratch?
Tesla has been working on this for almost 7 years (https://www.linkedin.com/in/ganesh-venkataramanan-99272a3/).
They might not work fast enough for your satisfaction but can you point any other car company designing chips like Dojo?
Hell, can you point to any company at all that can come out of the gate with a chip design competitive with the best design of NVIDIA, a company that makes nothing but chips?
After 7 years of work they are clearly confident that this will work or else they would scarp the project instead of giving talks at conferences.
Part of Tesla vertical integration mantra is that you are building internal capacity.
They are clearly confident that FSD will work, to the point of promising it "this year"...
coincidentally, they've been promising "this year" for the last 7 years. And FSD still fails in spectacular/hilarious/horrifying ways, and isn't _remotely_ close.
Being real for a second, Tesla don’t have an amazing track record of delivery on their ai software side of things.
Google.
But it seems clear to me that the point isn't to compete with these other companies, but to vertically integrate a critical component of their systems. They can discard a lot of legacy concerns and focus on raw power.
The thing is that if Tesla just uses Nvidia, like everyone else, then Tesla's stuff is only differentiated by software. Everyone uses Nvidia and it becomes hard to set themselves apart. But if Nvidia is chasing a broad customer base and has all this extra stuff to think about, then Tesla could potentially be more nimble, and produce a holistic system design that solves exactly the problems they have with no chuff. This could result in a more advanced hardware platform so their robotics products differentiate themselves with both hardware and software.
I am also happy because I am a robotics engineer, and in my opinion we need hardware that is 1000x more powerful than today to do what we really need. Nvidia wants to move at a certain pace, but if Tesla is trying to beat them on raw power, then Nvidia will play catch up and accelerate development of more powerful systems. This is great for everyone.
All the stuff you talked about about needing perfect support before you will touch it is something that takes a lot of work for nvidia and others, slowing them down. Tesla can ignore all that and focus on performance for their specific application, and I think this gives them the freedom to lead the pack on raw performance for their application.
I'm not sure if you've watched the presentations for how their self driving system is trained, but basically they have a million vehicles out in the real world with camera systems on them, and they have a massive server farm that is collecting new data from vehicles all the time, and they train their neural net by running millions of scenarios in simulation and against real world data collected from all those vehicles. And they have to re-train the system all the time with new data, and run it against old data to check for regressions. So they have this huge compute requirement to keep improving their system. They think that functional self driving will revolutionize their business (setting aside the valid criticism, this is what Tesla thinks) so they need to be able to handle an ever growing compute load that they have to be running constantly. So raw compute power is critical to the success of their plan. It may not be enough, but if they certainly can't succeed without it. But their needs are very specific, and it sounds like they've found an architecture which is simpler than most nvidia chips, but has loads of power. So it sounds like they are making a good decision, based on their specific needs. It is a huge risky bet, but then that's how Musk likes to do things.
This is surprising to me. Robotics clearly needs huge advances in algorithms (RL or something better). Do you mean you need faster hardware to discover those algorithms?
Looking at it another way, the human brain has wayyy more compute power than any of our current portable computers (robotics really needs to do most of its compute at the edge, on robot). Every robot I’ve ever worked with has been maxing out it’s CPU and GPU and still needed more compute.
When you look at Tesla’s hydra network for their self driving system you get an idea for what is needed in robotics, but just as we saw GPT-3 improve with network size, I suspect a lot of the base systems involved in a hydra net could improve with network size. And I suspect that there’s still more advanced stuff required when you move beyond a simple task like driving a car to a more advanced general purpose AI system. For example the Tesla self driving system doesn’t need any language centers, and we know GPT-3 and similar networks are large.
Why can't you hook your robot up to a GPU cluster? Tesla already has 7k+ A100 GPUs, the question is do they have algorithms which would clearly work better if only we could run them on 70k or 700k GPUs?
I mean, what you say makes sense, but have people actually tried all that and realized they need bigger GPU clusters? Is there a GPT-3 equivalent model in robotics which would probably work better if we scaled it up? If not, perhaps they should start with a small proof of concept before asking for thousands of GPUs. Original Transformer --> GPT1 --> GPT2 --> GPT3.
And no, the models I’m talking about don’t exist yet, I am speculating on what I think will be required based on how I see things. But I’m not asking for thousands of GPUs. I’m just speculating in internet comments about what robotics will need for a fully embodied AGI to function. I believe more edge compute power is needed. Perhaps 1000x more power than the Nvidia Orin edge compute for robotics today.
p.s. I don't think AGI is needed for robotics. I suspect that even an ant's level of intelligence (together with existing ML algorithms) might be enough for robots to do almost all the tasks we might want them to do today. It's ironic that robots today can see, read, speak, understand spoken commands, drive a car (more or less), play simple video games, and do a lot of other cool stuff, but still can't walk around an apartment without falling or getting stuck, do laundry, wash dishes, or cook food.
Doesn't the same apply to FSD? How many years is it now ready the next year?
Which is brilliant: they might end up making money on this.
AI is clearly here to stay. The demand for AI training will clearly explode in the future.
Running training in-house is not easy or cheap. You don't just plug in 1000 NVIDIA GPUs. You need massive up-front payment for GPUs and you're basically running your own extremely energy hungry datacenter .
Tesla might built and operate massive datacenters. They'll use as much as they need for internal needs and sell the remaining capacity to others.
This might take 5 years but the path to do it is clear.
For one, they’ve built a chip that operates in a fundamentally different way to other chips. So any other company that wanted to use it would have to invest a considerable amount of resources in building up the institutional knowledge to use it effectively.
Additionally, the lack of virtual memory and multi-tasking support renders it pretty much impossible to divide up compute between multiple customers. So, commercialising this would require customers renting out the whole unit, which is contrary to how cloud computing usually works.
Are there companies out there that have the capital and use cases necessary to fit into Dojo Cloud? Maybe, though not one I’ve worked for. Would they trust the stable genius currently heading up Tesla enough to make such an investment? Perhaps, but I wouldn’t, but what do I know?
Dojo has it's own IR but they also have a PyTorch to Dojo compiler.
People's opinion of Musk wont matter: either Dojo will be a capable service at a good price or it won't.
People will use it based on merits.
Such as? Except for FSD, his record is unmatched AFAIK when you take into account the novelty / complexity / difficulty.
One example, and certainly his main achievement: he said Tesla would sell and produce half a million cars by 2020, back in early 2014, and they hit that number with a 93.6% precision. https://youtu.be/BwUuo6e10AM?t=156
SpaceX? Great. Starlink? Sounds neat. Tesla? Pioneered electric cars with respectable performance and range.
But on the other hand, where's the hyperloop? Where's the affordable tunnelling? Where's the $35k Tesla - not available for order on the website, that's for sure. Where's the miniature submarine for rescuing children trapped in caves? Why has my buddy in Europe been waiting over a year for his powerwall to be delivered? Why are these norwegian tesla owners on hunger strike? Where's the full self driving, with taxi service? Why on earth would anyone want to buy Twitter?
Makes it very difficult to know which of Musk's statements are just spitballing, which are unrealistic timescale guesses and which can be relied on.
Getting any serious project architecturally 'locked in' to a special type of CPU you can only get from Tesla would be a bold move.
FSD, Hyperloop and such would be revolution like out of a Sci-Fi movie. They fail all because Musk like would like to have those things but in reality these things are much more complicated as he says.
He is good at marketing and developing existing technology.
But of his announced revolutions, none works.
seriously. musk is shady and weird, more so in the last couple of years, but come on.
Don't expect FSD in the near future and don't expect a Mars colony.
Next week he tweets he will take the service offline to buy AWS and then calls it off, that kind of reliability
Only if you want to subdivide the compute on each dojo chip. You can still provide multi-tenant, support by allocating entire dojo chips to a single customer at a time. Even traditional time division multi-tasking is possible as long as you’re happy to accept multi-second long time slices. Then the overhead of clearing an entire dojo chip (or batch of chips), and setting up a new application, isn’t too high.
If you’re doing AI workloads, then none of the above are an issue. Training a large net takes days to weeks of continuous, single task computation. So selling dojo access in whole 1 hour blocks is a perfectly reasonable thing to do.
The simples most obvious would be “give me your datasets and we’ll train your model”
Then talk about the upfront in house costs of setting up for GPU ops. But ignore that if an AWS style model works for you, well, AWS is already capable of giving it to you in GPUs.
Internal research product is super far from any actual production usage. Especially if you go against some established paradigms, that require enormous amount of effort (more than developing silicon) to build tolling around, so people can design, program, debug, monitor it.
But that’s internal usage. Cloud is a totally different ballgame. You have to deal with thousands more requirements (and you cannot generally tell customer to do something else instead, as you can with internal teams). And customers that have operating procedures totally different from yours, 0 access to your internal knowledge and infinite less tolerance for BS answers (as you are paying customer, not a someone on the same boat).
Building cloud is extremely hard, and there’s a reason why Google is still losing money on it.
Plus, let’s even say that your 5 year estimate is correct, Dojo is amazing and the future of tech and they may have viable product by then. Do you think that Nvidia wont advance their AI offering by then? Google TPU will stop being developed? Or will Tesla continue investing to churn new generation of Dojo every year?
You can. AWS started with S3 when everyone was using databases. As long as it’s cheaper than its competition, single use-case (you won’t serve a website on these) has a market.
AWS staryed when there was no competitor.
Google started with a ton of world-class expertise when AWS was up and running and while operating already a colossal network of server farms using special-purpose, which Tesla has none of which, and after all these years barely got a 10% market share.
Bold assumption, considering Tesla's hardware does not exist, the market is limited and Google has already years of providing machine learning services with special purpose hardware.
Sadly, not a very interesting one running UNICOS or NOS/VE...
"If we manage to put together a working processor, supporting hardware, OS, and possibly ad-hoc programming language, our next step is to also develop a bunch of web services to provide cloud hosting services."
Not very credible. As if the key to offer competing cloud hosting services is developing the whole hardware and software stack.
The key difference between this goal and SpaceX is that Elon Musk bought a private space company that already had the know-how and the market presence in a market with virtually no competitor.
In this case, Tesla is posting wild claims about developing the whole vertical integration of the whole tech stack barely over mining semiconductor raw materials, with which goal? Competing with the likes of AWS, Google, and Microsoft, on a very niche market?
Digging holes in the ground is hardly rocket science as well.
AMD chips like MI250x are 5nm with tensor matrix multiplication units. NVidia Hopper will be like 4nm IIRC?
They've already been working on this for almost 7 years. This is not some side projects but a serious operation. They have to ship something sometime and they decided to ship this now.
I'm confident that they know about 5nm process and chose 7nm for good reasons.
There will be next version and next process.
Implementing a process shrink is not just scaling the masks by the appropriate percentage. It often is a completely different optical train, at a different wavelength, different pellicle, changing the refractive index of the immersion fluid, different multiple patterning. It takes months (years?) of work.
For many applications it's worth it, but it's not at all a slam dunk. The vast majority of ASICs are designed for a particular node and never upgraded. It's the kind of crazy long-term speculative capital investment TSLA might have indulged in when the stock was at $414 a share, but it's nowhere remotely near that today.
They're already engaging in layoffs, today. Why spend the money?
They are, if the new node is cheap enough to justify the investment in shrinking the design. In most ASICs, being faster won’t make people rip out their embedded electronics for new models.
You both have limited availability of the node and limited availability of the particular chip.
Plus you are paying the margin of that company.
So power efficiency becomes king, and process node is the best way to minimize power consumption.
Tesla is big enough that they can afford to experiment a little. This sounds like an experiment that actually worked. Once they had it working, doing more of it and iterating on it is just business as usual for them.
Are you sure? We haven’t even seen the chip in any physical form yet. We haven’t seen it running any code.
And the D1 is for their training machine anyways. Not their inference chip in the cars
From my point of view, Tesla is converging on good enough more rapidly than anyone else in the industry. Courtesy of their AI strategy and technology; including the in house developed chips.
This is a big problem, though. Any ADAS is a safety critical system and while "good enough" can help push volumes, it is extremely bad from safety perspective. If you really want to read on this u/adamjosephcook/ has some awesome writing going into far deeper detail than I could explaining this particular issue.
Even if that increases the stock price by 1%, this investment pays for itself a hundred times over.
The $100-million chip might very well be the easy part of this project...
I doubt developing a compiler for this chip will be as costly as your estimate, especially given mature opensource compiler frameworks like LLVM (already adapted to similar TPU-like architectures) and upcoming pytorch glow, apache TVM ML-specific compiler frameworks. And especially given in our industry there are people with advanced degrees who are willing to jump through additional hoops and even accept pay cuts, just to work on a cool compiler.
I respect the hardware for what it is and what it could enable.
But is that really the intention? Wouldn't it be enough if there is one program written for it, one that takes a bunch of inputs and a bunch of outputs and then creates a set of NN weights that perform well?
So the AWS style offer would not be "rent our hardware and program on it" but "rent our hardware/software stack, throw your input/output pairs at it and get back a set (billions) of NN weights that perform well".
Or because the program behaves differently in different places, and it is too big to optimize.
I honestly can't see how they make (or save) enough money with just the data center chip here.
Maybe if the inference chip is on a cheaper 12nm process or something... Maybe?
It's about accelerating nn training and winning robotaxi market, which could make them the most profitable company ever.
The question is if they are saving money compared to buying an off the shelf DGX system. I really doubt it.
https://www.tomshardware.com/news/tesla-brags-about-in-house...
So... no? They are clearly leveraging the NVidia ecosystem right now. Now maybe they have ambitions to get off of NVidia, but they're doing so in a rather asinine fashion. There's probably half-a-dozen groups trying to make a faster systolic matrix multiplication unit for the deep learning crowd. Tesla probably should have either worked with those groups and/or bought one out, for example.
Tesla is (also) an AI company. Musk is thinking 10 years ahead.
If you read the resumes of people leading FSD chip and DOJO: those are chip design industry veterans that Musk hired away from AMD, Apple and other.
He built a stronger team than whatever startup you think he could buy.
Those people have already been working on Dojo for over 6 years and they'll be working for next 20.
https://www.linkedin.com/in/ganesh-venkataramanan-99272a3/ is the lead for Dojo and has been at Tesla for 6.7 years.
Tesla can hire they best people and give them practically unlimited funding, certainly much bigger than any startup could afford.
This is a big, bold, risky bet. Kind of like reusable rockets or going to mars.
So now they'll need porting effort to move their GPU-based current design to this new, very non-GPU, instruction set.
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Except step 1: "design a chip", is already something on the order of hundreds-of-megabucks of investment
but they're already there, looks like.
And the other tidbit is that it has to be better than the off the shelf NVidia chip (A100 today, and GH100 in a few months).
I think Dojo might be faster than A100, but GH100 will be 1.5 nodes ahead, that's a big process advantage...
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If GH100 is faster in practice, then all the work has been somewhat wasted.
Presumably Tesla, at the time they decided to pursue this option, thought they'd potentially have a competitive advantage with in-house designs.
It's entirely possible that's just their hubris showing, time will tell if this was the right decision or not. After seeing the NVidia presentation announcing their latest datacenter-scale AI hardware, I'd be surprised if Tesla's in-house design is more than just a massive cost center vs. buying something from NVidia.
But sometimes you do things that appear irrational in part to keep your talented engineers from seeking work elsewhere. Just look at NASA's SLS, how much of that is a job program in part to prevent hoards of talented folks building rockets for competing nations?
Did we watch the same presentation? NVidia knocked it out of the park.
Thread block cluster is obviously amazing. Routing between SMs / compute units will be far faster with this level of software abstraction, and it will be exceptionally easy to write code for. NVidia always impresses me with their advanced software techniques and clear understanding of the fundamental model of SIMD compute.
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Ignoring those software details... the important thing is GH100 will be TSMC 4nm, which is 1.5 nodes ahead of the 7nm Dojo. A significant process advantage, representing 60+% less power usage and 300% the transistor density of the older 7nm tech.
Even if NVidia's GPU had issues, there's something to be said about just being a raw process node (or 1.5 nodes) ahead.
Perhaps I worded it poorly, I agree with you.
My meaning was vs. NVidia's latest tech it seems like Tesla's in-house datacenter NN could be nothing more than a huge cost center without even offering an advantage over what NVidia could sell them.
But like I said, if you have a staff of folks capable of building such things you have to keep them satisfied with practicing their craft or they leave.
People doing this are not some bored Tesla engineers.
Both the FSD chip and Dojo are staffed by chip design veterans Tesla poached from AMD, Apple and others.
Just read those resumes:
https://www.linkedin.com/in/peterbannon/ : this is the guy leading FSD chip
https://www.linkedin.com/in/ganesh-venkataramanan-99272a3/ : this is the guy leading Dojo
People like this can only be poached with a combination of great project and great salary.
It's a team that was built from grounds up because Tesla is (also) an AI company and Musk is thinking 10 years ahead.
The whole reason they're not bored is because they're doing this.
You do realize we're basically in agreement, right? The more impressive the resumes the more important it is that you give them Real Work to do.
But NVidia's state of the art in this space seems substantially better than Dojo. How much that matters in totality remains to be seen.
Well, also because Tesla has an unnaturally low cost of capital because of its meme stock status.
For your convenience, and you will want to look mainly at 10K and 10Q forms: https://www.nasdaq.com/market-activity/stocks/tsla/sec-filin...
Zero. Because NASA has no problem if those engineers would work for other nation as long as it isn't Russia or North Korea and co. And that wouldn't happen anyway.
Those people would likely work in one of the huge amount of space startups or just go to the typical ULA, BlueOrigin, SpaceX and so on.
You make the totally wrong assumption that SLS has anything to do with rational thought. It really doesn't.
Although the article doesn’t look to address thermal issues from my summary skim of it.
Yes, but anyone can get them - they aren’t a competitive advantage.
AMD pays for this flexibility by spending about 44% of “Zeppelin” die area on logic other than cores and cache. Dojo only spends 28.9% of die area on things other than SRAM and cores.
For comparison, the fastest single-chip GPU today is the AMD MI250X which has 220 "cores" (compute units) totaling 383 BF16 TFLOPS at 1.7 GHz, and that's a monster 560 watt chip.
The Dojo chip is likely under this 560 was TDP, so more efficient. And Tesla provides roughly the same compute performance, but the chip is arrayed in 61% more cores, meaning it is far more suitable to handle branchy code. Also Tesla claims the die measures only 645 mm², compared to 1540 mm² for AMD. So the wafer fabrication cost is roughly half(!) of AMD's!
If Tesla has truly managed to build that, I'm impressed.
Edit: I missed that Tesla claims "less than 600 watt" per chip. So we know its comparable or less than AMD (560 watt).
Edit 2: 25 dies are packed on a single system-on-wafer. That's 15 kW on a disc of 30 cm (12 in) of diameter. Sheesh! That must require an ungodly liquid cooling system!
Edit 3: there is more info, including rendering of the host interface card at:
https://www.servethehome.com/tesla-dojo-custom-ai-supercompu... and
https://www.servethehome.com/tesla-dojo-ai-system-microarchi...
Edit 4: found a pic of the liquid cooling system - as expected ;) https://media.datacenterdynamics.com/media/images/training_t... source: https://www.datacenterdynamics.com/en/news/tesla-details-doj... And they say the first tile was "tested last week" as of August 20th... This confirms my suspicion that the system is barely (?) functional. Also see "Venkataramanan appeared to even surprised Andrej Karpathy, Tesla’s head of AI, on stage by revealing for the first time that Dojo training tile ran one of his neural networks" from https://electrek.co/2021/08/20/tesla-dojo-supercomputer-worl...
Nvidia RTX 3090: 285 Tensor-TOPs @ 350 W [1]
Specialised accelerator: 100 TOPS at less than four watts [2]
[1]: https://wccftech.com/nvidia-geforce-rtx-3090-geforce-rtx-308...
I know it must be horrendously difficult to program for general purpose use, but that's the point - figuring out what CAN be done and what an OS for it would look like.
In any case the problem is moving data, then the compute side. If Tesla is really going to do it they have someone working on that already. SAAS is not rocket science, and the issues are well-known.
This is a chip designed to accelerate nn training in their datacenter.
The effort and expense makes perfect sense if you consider:
- the costs are spread over millions of car (3 millions, today, tens of millions in the future)
- it helps them win robotaxi market that is potentially so lucrative that the cost of developing custom chips will be peanuts
That would be typical of Elon Musk to get some federal funding $$$$$
but Elon can make it work on par if not better than nVidia, just because Tesla can hardcode and overoptimize for one single task (FSD AI), while nVidia will always be limited in keeping their chips generic enough for all purpose NN training/inference and even gaming + backwards compatibility for all historical stuff.
Tesla can work with blank slate and can reap performance benefits over there
That's an extremely complicated thing that requires armies of PhDs (chem, EE, physics, etc.) and billions of dollars.
You can think of what this is as 'maps' that manufacturers instantiate as chips.
IDK what tools Tesla is actually using, but for context, the work that Tesla employees would do looks like this: https://www.cadence.com/en_US/home/tools/custom-ic-analog-rf...
So no, your conspiracy theory does not align with reality.
Certainly you can build custom chips for deep learning. It's mostly a simple inner loop, replicated many times, after all. Ideal case for ASICs. But will this actually benefit Tesla as a car company?
This is a hype project simply created to pump up the stock price.
After first AI day Tesla stock dropped significantly. Wall Street doesn't understand Tesla as anything else but a car company and won't understand AI until it start producing billions in revenues.
It's exceedingly poor stock pump.
They've been working on Dojo for almost 7 years. They kept it secret for the first 5.
Kind of contradicts the "stock pump" memes.
They start talking about it now because those efforts are very advanced and they want to hire more people.
> two incredibly resource-intense and error-prone processes in the same company (Automotive, Semiconductor).
If you think making these chips is excessive, you don't know Tesla. Tesla is the most vertically integrated car company in the world (maybe BYD compares).
Compared to literally designing not only their own batteries cells, but their own battery chemistries plus their own battery materials process AND their own fully up battery manufacturing factories. They started doing this a decade ago and only this year the first cars rolled of the line and being sold to costumers. That is years and years of investment, planning and internal capacity building.
In 2017 people were laughing at them for attempting to do all their own manufactriong. Now they have industry dominating automotive margin. They literally have their own materials team that makes their own aluminum for casting and have co-developed production machines that are the size of a whole house. This is now slowly being copied by other manufactures.
They also make their own glass, seats and a whole host of other things that most car companies don't make. Doing these things in-house cost billions and billions in capx.
Also, Dojo is not the only chip they make, they have other chips that are already being sold in their cars by the millions every year. Dojo is a pittance of investment compared to that and a fraction of the total investment into the overall self-driving stack. Just because you don't agree with the overall strategy (assuming from your comment) doesn't mean that they many, many billions Tesla spends isn't real, or its all some sort of stock price play.
So, if you think Dojo is about stock price, I don't know what to tell you, its just wrong.
However I do agree that Dojo isn’t about the stock price though I’m sure it’s about reducing their bottom line costs long term.
Weird. I though competition, even when the Leader is far ahead is a good thing and up to the Investors.
No idea whether they can make it work, it is really hard, especially adjusting their tools, but they have written most tools themselves, so adjusting should be possible. Making those tools available to outsiders one day ? can happen, doesn't have to.
A non-issue if you understand the design space of TPU-like AI accelerators. The understanding shouldn't even be necessary because the major players (Google, now Amazon) already build their TPUs and have been for years.
Nvidia tax is too much.
With Musk, everything is a step towards the end goal(s), like colonizing Mars (which might even be the only goal since it ties everything together; it’s much easier to send humanoids to prepare for colonization and it’s not like HCI seems at all a priority unlike many other AI efforts).
My guess is that with Tesla, Musk aims to create AGI in a way he can control (as you may recall, he has previously talked about fears of the tech and control is a natural strategy to handle this). The cars are just the step that enables the next, so he basically just invests in the future over short term gains, which is a very reasonable business decision.
Custom silicon is likely unavoidable if you want to lead the AGI field, so he would just be investing in the next step of the plan. Tesla Bot is a good indicator of this direction.
Tesla Bot was the human in a bot costume dancing on stage [0], right? If so, then yes, this is quite a good indicator of the seriousness of this direction.
Did we ever get anything more substantive about Tesla Bot though?