Tesla Superchargers in Taiwan with Tesla and CCS connectors
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The CCS (Combined Charging System) has become the standard for electric vehicle charging over the last few
years.
When Tesla first debuted the 2012 Model S, the CCS charging connector didn't exist. In fact Tesla developed
its proprietary Tesla connector because there wasn't anything capable of fast DC charging.
Today, the CCS connector supports charging speeds up to 350kW.
Tesla already offers Superchargers with CCS connectors in several regions, but they will now begin adding CCS
connectors to Superchargers in the United States.
Tesla will add the CCS connector in addition to Tesla's own connector. This will give non-Tesla owners access
the extensive charging network, Elon Musk said.
Non-Tesla electric cars have been allowed to charge at select Tesla Supercharger locations in France, the
Netherlands, and Norway since November.
Allowing Superchargers - which account for more than half of all fast chargers in the United States to charge
all electric vehicles would be easier and less expensive for everyone involved, and it would substantially
improve the landscape of the current fast-charging infrastructure.
CCS is the obvious charging standard to go with, given that Tesla, like many other manufacturers, has already
accepted CCS standards in Europe and its Supercharger stations are already equipped with CCS connectors.
Tesla's cars and Supercharger stations in North America use its own proprietary connector, which has rendered
Non-Tesla owners unable to use Tesla's fast-charging infrastructure.
It also prevents Tesla owners from charging at other DC charging stations, unless they spend a considerable
amount of money purchasing a CHAdeMO or CCS adapter.
Speaking at the Financial Times Future of the Car summit, Musk said they will add the connectors even if it
lessens their competitive advantage over other automakers.
“It's a little trickier in the US because we have a different connector than the rest of the industry, but we
will be adding the rest of the industry connectors as an option to Superchargers in the US. We are trying as
best as possible to do the right thing for the advancement of electrification, even if that diminishes our
competitive advantage,” Musk said.
This is comparable to Tesla's approach in Europe when the Model 3 was originally introduced with the CCS
standard. Both Tesla and CCS connectors were installed at new Supercharger stations, and the carmaker also
began retrofitting some existing stations.
Last year, the Taiwan EV Charger Equipment Supplier and Manufacturer Advancement Alliance declared that CCS
should be the country's charging standard, forcing Tesla to retrofit CCS connectors to all
Superchargers.
Tesla upgraded Superchargers with CCS connectors in addition to their proprietary connectors a few months
after the decision.
Tesla's CEO gave no indication of when the company planned to begin installing CCS connectors at stations in
the United States.
Is Your Vehicle Compatible?
The connector the US is using differs slightly from the CCS connector in Europe. In the US it's known as CCS combo 1, or CCS1 for short. This is the connector that Tesla will support in the US and it is not interchangeable with CCS2 that is used in Europe.
Tesla is already selling an adapter to go from CCS1 to Tesla's plug, but it is currently only available in South Korea. Tesla is likely to make this adapter available for sale in the US in the future.
However, your Tesla will need to specifically support the CCS adapter. If your Tesla was built after May 2019, then it likely supports the CCS adapter. If it was before then, then it will need to be retrofitted if you plan to charge using the CCS 1 adapter.
You can check whether your car supports the CCS adapter by going to Controls > Software and tapping Additional Vehicle Information.
You can also find more information about how to check whether your car is supported, the cost of a retrofit, and the cost of the adapter in our CCS adapter article.
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Tesla launched two FSD updates simultaneously on Saturday night, and what’s most interesting is that they arrived on the same software version. We’ll dig into that a little later, but for now, there’s good news for everyone. For Hardware 3 owners, FSD V12.6.1 is launching to all vehicles, including the Model 3 and Model Y. For AI4 owners, FSD V13.2.4 is launching, starting with the Cybertruck.
FSD V13.2.4
A new V13 build is now rolling out to the Cybertruck and is expected to arrive for the rest of the AI4 fleet soon. However, this build seems to be focused on bug fixes. There are no changes to the release notes for the Cybertruck with this release, and it’s unlikely to feature any changes when it arrives on other vehicles.
FSD V12.6.1 builds upon V12.6, which is the latest FSD version for HW3 vehicles. While FSD V12.6 was only released for the redesigned Model S and Model X with HW3, FSD V12.6.1 is adding support for the Model 3 and Model Y.
While this is only a bug-fix release for users coming from FSD V12.6, it includes massive improvements for anyone coming from an older FSD version. Two of the biggest changes are the new end-to-end highway stack that now utilizes FSD V12 for highway driving and a redesigned controller that allows FSD to drive “V13” smooth.
It also adds speed profiles, earlier lane changes, and more. You can read our in-depth look at all the changes in FSD V12.6.
Same Update, Multiple FSD Builds
What’s interesting about this software version is that it “includes" two FSD updates, V12.6.1 for HW3 and V13.2.4 for HW4 vehicles. While this is interesting, it’s less special when you understand what’s happening under the hood.
The vehicle’s firmware and Autopilot firmware are actually completely separate. While a vehicle downloading a firmware update may look like a singular process, it’s actually performing several functions during this period. First, it downloads the vehicle’s firmware. Upon unpacking the update, it’s instructed which Autopilot/FSD firmware should be downloaded.
While the FSD firmware is separate, the vehicle can’t download any FSD update. The FSD version is hard-coded in the vehicle’s firmware that was just downloaded. This helps Tesla keep the infotainment and Autopilot firmware tightly coupled, leading to fewer issues.
What we’re seeing here is that HW3 vehicles are being told to download one FSD version, while HW4 vehicles are being told to download a different version.
While this is the first time Tesla has had two FSD versions tied to the same vehicle software version, the process hasn’t actually changed, and what we’re seeing won’t lead to faster FSD updates or the ability to download FSD separately. What we’re seeing is the direct result of the divergence of HW3 and HW4.
While HW3/4 remained basically on the same FSD version until recently, it is now necessary to deploy different versions for the two platforms. We expect this to be the norm going forward, where HW3 will be on a much different version of FSD than HW4. While each update may not include two different FSD versions going forward, we may see it occasionally, depending on which features Autopilot is dependent on.
Thanks to Greentheonly for helping us understand what happened with this release and for the insight into Tesla’s processes.
At the 2025 Consumer Electronics Show, Nvidia showed off its new consumer graphics cards, home-scale compute machines, and commercial AI offerings. One of these offerings included the new Nvidia Cosmos training system.
Nvidia is a close partner of Tesla - in fact, they produce and supply the GPUs that Tesla uses to train FSD - the H100s and soon-to-be H200s, located at the new Cortex Supercomputing Cluster at Giga Texas. Nvidia will also challenge Tesla’s lead in developing and deploying synthetic training data for an autonomous driving system - something Tesla is already doing.
However, this is far more important for other manufacturers. We’re going to take a look at what Nvidia is offering and how it compares to what Tesla is already doing. We’ve done a few deep dives into how Tesla’s FSD works, how Tesla streamlines FSD, and, more recently, how they optimize FSD. If you want to get familiar with a bit of the lingo and the background knowledge, we recommend reading those articles before continuing, but we’ll do our best to explain how all this synthetic data works.
Nvidia Cosmos
Nvidia’s Cosmos is a generative AI model created to accelerate the development of physical AI systems, including robots and autonomous vehicles. Remember - Tesla’s FSD is also the same software that powers their humanoid robot, Optimus. Nvidia is aiming to tackle physical, real-world deployments of AI anywhere from your home, your street, or your workplace, just like Tesla.
Cosmos is a physics-aware engine that learns from real-world video and builds simulated video inputs. It tokenizes data to help AI systems learn quicker, all based on the video that is input into the system. Sound familiar? That’s exactly how FSD learns as well.
Cosmos also has the capability to do sensor-fused simulations. That means it can take multiple input sources - video, LiDAR, audio, or whatever else the user intends, and fuse them together into a single-world simulation for your AI model to learn from. This helps train, test, and validate autonomous vehicle behavior in a safe, synthetic format while also providing a massive breadth of data.
Data Scaling
Of course, Cosmos itself still requires video input - the more video you feed it, the more simulations it can generate and run. Data scaling is a necessity for AI applications, as you’ll need to feed it an infinite amount of data to build an infinite amount of scenarios for it to train itself on.
Synthetic data also has a problem - is it real? Can it predict real-world situations? In early 2024, Elon Musk commented on this problem, noting that data scales infinitely both in the real world and in simulated data. A better way to gather testing data is through real-world data. After all, no AI can predict the real world just yet - in fact, that’s an excellent quantum computing problem that the brightest minds are working on.
Yun-Ta Tsai, an engineer at Tesla’s AI team, also mentioned that writing code or generating scenarios doesn’t cover what even the wildest AI hallucinations might come up with. There are lots of optical phenomena and real-world situations that don’t necessarily make sense in the rigid training sets that AI would develop, so real-world data is absolutely essential to build a system that can actually train a useful real-world AI.
Tesla has billions of miles of real-world video that can be used for training, according to Tesla’s Social Media Team Lead Viv. This much data is essential because even today, FSD encounters “edge cases” that can confuse it, slow it down, or render it incapable of continuing, throwing up the dreaded red hands telling the user to take over.
Cosmos was trained on approximately 20 million hours of footage, including human activities like walking and manipulating objects. On the other hand, Tesla’s fleet gathers approximately 2,380 recorded minutes of real-world video per minute. Every 140 hours - just shy of 6 days - Tesla’s fleet gathers 20 million hours of footage. That was a little bit of back-of-the-napkin math, calculated at 60 mph as the average speed.
Generative Worlds
Both Tesla’s FSD and Nvidia’s Cosmos can generate highly realistic, physics-based worlds. These worlds are life-like environments and simulate the movement of people and traffic and the real-life position of obstacles and objects, including curbs, fences, buildings, and other objects.
Tesla uses a combination of real-world data and synthetic data, but the combination of data is heavily weighted to real-world data. Meanwhile, companies who use Cosmos will be weighting their data heavily towards synthetically created situations, drastically limiting what kind of cases they may see in their training datasets.
As such, while generative worlds may be useful to validate an AI quickly, we would argue that these worlds aren’t as useful as real-world data to do the training of an AI.
Overall, Cosmos is an exciting step - others are clearly following in Tesla’s footsteps, but they’re extremely far behind in real-world data. Tesla has built a massive first-mover advantage in AI and autonomy, and others are now playing catch-up.
We’re excited to see how Tesla’s future deployment of its Dojo Supercomputer for Data Labelling adds to its pre-existing lead, and how Cortex will be able to expand, as well as what competitors are going to be bringing to the table. After all, competition breeds innovation - and that’s how Tesla innovated in the EV space to begin with.
