Last night Tesla rolled out update 2024.26.15 which included FSD v12.5.1.4. Although the release notes remain the same with this update, the big news is that Tesla has finally added support for hardware 3 vehicles to this FSD 12.5 version.
Once the release went out, we heard rumblings early on that update 2024.26.15 may add support for hardware 3, however, today we’ve been able to confirm that Tesla has HW3 vehicles running this latest FSD update.
Elon Musk initially said that FSD 12.5 was expected to be available for HW3 in about 10 days after it rolled out to AI4. Although that date has come and passed, it finally arrived for employees last night. Although It’s only been about three weeks, it felt like an eternity for HW3 owners who have been anxiously awaiting this update. Many owners weren’t sure whether their vehicles would be able to run the latest version of FSD once the original goal of 10 days was missed. This is one area where Tesla can stand to improve — consumer communication.
Rollout Plan
Although Tesla has internal HW3 vehicles running update FSD 12.5.1.4, their public rollout plan may not go to HW3 vehicles initially. Tesla may choose to roll out the update to vehicles with HW4 first to confirm there are no regressions before proceeding with the launch on HW3 vehicles.
Keep in mind that not every release that goes to employees ends up going out to the public. However, most releases usually start rolling out to early-access customers within a few days if there are no major issues.
HW3 vehicles are currently running FSD v12.3.6, while most HW4 (AI4) vehicles are running FSD v12.5.1.3. FSD v12.5 in general includes a huge array of optimizations and is supposed to be as big an improvement as the capability jump between v11 and v12.
We don’t expect any new features in FSD v12.5.1.4 besides HW3 support as that would complicate solving any potential regressions. However, we’re still awaiting several new features that come in a later version of FSD v12.5 such as nag-less FSD using sunglasses, FSD support on the Cybertruck, improved lane changes, end-to-end neural networks on the highway, and even Actually Smart Summon.
What’s Included & Who’s Eligible
This update is based on Tesla’s 2024.26 code branch, meaning that everyone who subscribes or owns FSD will be eligible to receive it. Tesla is already testing update 2024.32, but it hasn’t gone out to customers yet.
FSD V12.5 rolled out to AI4 vehicles much earlier than hardware 3 vehicles – as code optimization was required to have the FSD model run on the older hardware. It seems that the extra compute offered by AI4 vehicles has allowed Tesla to release V12.5 faster, which also helps them gather more data.
Now that HW3 support has been released, this could set the new tempo for new updates. Major FSD updates – V12.6 or V12.7 will likely also have AI4 support before hardware 3 support, as it seems easier to develop with more hardware power and then later optimize code once you have a working product. However, Tesla has committed to maintaining hardware 3 support, even if the code requires optimization to function on older hardware.
Divergence
AI4 only really started rolling out in the middle of last year, with most customer vehicles arriving with AI4 near May 2023. The optimization gap has been a surprise, especially since AI4 is still running hardware 3 in emulation mode. There will be a divergence between AI4 and Hardware 3, and Elon has mentioned that the new supercomputer cluster will be dedicated to preparing for AI4, and in the future, AI5.
This is going to be a big one for most people – after all, we’re estimating that nearly 85% of Tesla’s current fleet, if not more, is still on hardware 3. Did Tesla manage to fit the complete AI model, with all its parameters – a 6x increase over V12.3.6 – onto Hardware 3?
If there were compromises, we’ll need to watch and see. Hopefully, some enterprising Tesla X or YouTube owners will record a test drive on v12.5.1.4 on both, a HW3 and AI4 vehicle for us to compare the differences. We’re hoping that since the official divergence of hardware hasn’t happened yet, at this point, we won’t see any differences.
Upcoming Features
Once FSD v12.5 rolls out to hardware 3 vehicles, we can look forward to the upcoming list of features, including more natural and better-planned turns, the E2N highway stack, and vision-only monitoring with sunglasses. Of course, there are a few other hotly anticipated features we’re waiting for, but which aren’t on the Upcoming Improvements list provided in the patch notes – including Park Seek and Banish Autopark, as well as vehicle-to-fleet communications and FSD understanding hand gestures.
We’re hoping to see these features come in point releases of FSD V12.5, which means they should arrive at similar times for HW3 and AI4 vehicles.
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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.
