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Tesla details its self-driving Supercomputer that will bring in the Dojo era

(Credit: Tesla)

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Tesla’s Senior Director of Artificial Intelligence, Andrej Karpathy, detailed the automaker’s self-driving Supercomputer that will era in Dojo. Dojo is Tesla’s 4-dimensional Autopilot training program and was first discussed in 2020 by CEO Elon Musk. Karpathy gave a 40-minute presentation regarding Autonomous Vehicles at the 2021 Conference on Computer Vision and Pattern Recognition (CVPR 2021).

Tesla has been working on its self-driving program for several years, and through that time, it has trained its Autopilot and Full Self-Driving suite by using a Neural Network to make it more accurate and robust in its movement. As a result, Tesla has established itself as one of the companies with the most robust semi-autonomous driving programs globally.

Despite this, Tesla continues to make strides to improve it even further. This started back in August 2020, when CEO Elon Musk detailed Dojo, a 4-dimensional training program that will process monumental volumes of video data. This aligns with Tesla’s recent decision to adopt a Vision-only approach in its vehicles, ditching radar in the Model 3 and Model Y.

Musk said:

“Tesla is developing a NN training computer called Dojo to process truly vast amounts of video data. It’s a beast! Please consider joining our AI or computer/chip teams if this sounds interesting.”

For a long time, Tesla has worked with what Musk called “just been like 2D.” He said that the 4D system would work tremendously better since it’s basically video.

During the Q2 2020 Earnings Call, Musk said:

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“So what we’ve been doing, thus far, has really just been like 2D — mostly 2D, and like I said, well correlated in time. So just hard to convey just how much better a fully 4D system would work — does work. It’s capable of things that if you just look — looking at things as individual pictures as opposed to video — basically, like you could go from like individual pictures to surround video, so it’s fundamental. So the car will seem to have just like a giant improvement.”

Now that Tesla is moving closer to the completion of Dojo, Karpathy talked about the Neural Network, Supercomputers, and the excellent work of Tesla’s Supercomputing team.

In Karpathy’s presentation, the AI head stated that there are three main factors to get a Neural Network signal to work: Large amounts of video, clean data, and diverse scenarios to make the suite as well-rounded as possible. It is evident that through Tesla’s decision to make its two mass-market vehicles vision-based, that large amounts of data is one of the biggest factors. The Model 3 and Model Y have dominated Tesla’s sales numbers globally for some time, meaning they contribute more data to the Neural Network than the other two vehicles in Tesla’s fleet.

Now that Dojo has been in development for some time, Karpathy unveiled some details about the Supercomputer that Tesla uses for data consumption and storage. A slide in the presentation details the specifications:

  • 720 nodes of 8x A100 80 GB (5760 GPUs total)
  • 1.8 EFLOPS (720 nodes * 312 TFLOPS-FP16-A100 * 8 GPU/nodes)
  • 10 PB of “hot tier” NVME storage @ 1.6 TBps
  • 640 Tbps of total switching capacity

Credit: Yarrow B. | YouTube

Karpathy said that these specs make it “roughly the number five Supercomputer in the world” during his presentation.

Now, Dojo is still not released, and Karpathy was unwilling to comment further on the progress or any of the finer points of what will likely bring Tesla close to Level 5 autonomy. Still, there should be more details in the coming months. Tesla has stuck by the 2021 time frame for Dojo since first talking about it, so hopefully, the company will shed more detail on it during the Q2 or Q3 2021 Earnings Call.

Karpathy’s full presentation is available below.

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Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com

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Elon Musk

Tesla CEO Elon Musk denies ridiculous Gigafactory Shanghai rumor

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(Credit: Tesla)

Tesla CEO Elon Musk took to his social media platform X on Thursday night to deny a ridiculous rumor regarding the sale of the company’s Chinese vehicle production plant, Gigafactory Shanghai.

On Thursday, the Wall Street Journal, citing sources familiar with the matter, claimed in a scathing new report that Tesla was exploring a potential sale of the entire China business in an effort to help bolster a potential merger between SpaceX and Tesla.

Musk immediately denied the rumor not once but twice, initially calling it “fake news,” and then calling it “absurdly fake news” in a separate post just a few moments later:

The original poster of the Wall Street Journal article that Musk saw deleted the initial post sharing the headline and the rumored sale of Tesla’s China business.

The report seemed absolutely and unequivocally false to begin with; Tesla’s business in China is among the most important pieces of the company’s business. Not only does the factory supply vehicles for the domestic market, but also for various other markets in Asia and Europe.

China is also one of the largest automotive markets in the world, and Tesla has performed well there despite the robust competition.

The speculation regarding a Tesla and SpaceX merger has started to gain steam this year as the space exploration company went public just a month ago. There has been speculation that Musk will bridge all of his companies under one “umbrella company,” and analysts believe this could happen before the end of the decade.

The Tesla and SpaceX merger everyone is talking about is quietly building

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This is the latest iteration of Musk’s very evident war on mainstream media. Reports regarding any of Musk’s companies are quick to get the dreaded “false” or “fake news” response from the CEO when they are unfounded.

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Tesla AI boss reveals how big Optimus is going to get

Tesla’s Optimus chief corrected himself on X, confirming a staggering 10 million robot production target.

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Tesla Optimus Gen 3 [Credit: Tesla]

Tesla’s Optimus program has a new number attached to it, after Ashok Elluswamy, the executive who has run the humanoid robot program since June 2025, posted a three word correction on X Thursday, “Correction, 10 million robots.”

The line clarifies the long term annual capacity Tesla is building toward its planned second Optimus production line at Gigafactory Texas, a figure Musk has cited repeatedly since last year’s shareholder meeting.

The scale is worth noting, because ten million robots a year would mean Tesla building more units annually than most countries sell in new cars. Tesla has framed this as a second line, not the first. The buildout is happening in two phases: a roughly one million unit per year line inside Tesla’s Fremont factory, installed on the floor space vacated when Model S and Model X production ended earlier this year, and a much larger dedicated facility under construction at Giga Texas that broke ground on its first steel structure in May. That Texas facility is the one Elluswamy’s correction refers to, and is expected to reach volume production sometime in 2027.

Tesla Optimus project fires up as Musk sees production line progress

Elluswamy took over Optimus from Milan Kovac last summer and has spent the months since talking up the program’s trajectory. Elon Musk has also floated the ten million figure at Tesla’s 2025 shareholder meeting.

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Ending Model S and Model X production to make room for the first Optimus line was one of the more consequential manufacturing decisions in the company’s recent history, retiring two flagship vehicles in favor of a robot that has yet to enter mass production. Musk has previously estimated per unit production costs at $20,000 to $25,000 once Tesla reaches a million units a year, though he hasn’t said what that cost looks like at ten times the volume.

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Autonomous vehicle red tape gets slashed by Trump Administration

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Credit: Zoox

The Trump Administration today made several key moves to help with the deployment of autonomous vehicles by cutting overreaching red tape that has stifled growth and innovation for years.

The moves, which were put forth by the National Highway Traffic Safety Administration (NHTSA), aim to grant temporary exemptions to at least one company currently, although that could expand in the coming months. Additionally, it will work with organizations to develop standards and a sound but efficient regulatory landscape.

Zoox is the only company mentioned explicitly by the Trump Administration in its press release announcing the new terms today. They will receive a temporary two-year exemption that will allow the commercial deployment of up to 2,500 vehicles annually for two years.

There is a potential exemption for Robomart, Inc., which “requests a temporary exemption from certain FMVSS No. 500 requirements for a low-speed vehicle operated by an ADS without a human driver onboard. NHTSA will publish a separate notice seeking public comment on its merits once the initial evaluation is complete,” the agency said.

Here are the five new terms that Secretary Sean Duffy has implemented through the NHTSA today:

  1. Allow Zoox to commercially deploy its robotaxis through a temporary exemption.
    This temporary exemption will allow the commercial deployment of up to 2,500 vehicles annually for two years, subject to an enhanced, adaptable oversight structure that can evolve as Zoox’s technology advances.
  2. Accelerate development of first-ever AV performance standards through a partnership with SAE Industry Technologies Consortia (ITC).
    This partnership will fund a three-year, $5 million “A2SCEND” consortium, bringing together experts to gather data and accelerate creation of the first-ever AV performance standards. This project will inform a single national standard for AV safety to eliminate the patchwork regulatory landscape that has stifled innovation for years.
  3. Publish an interim final rule that allows vehicles manufactured prior to an exemption to be eligible for a commercial deployment exemption.
    This rule will modernize the application process and improve access to exemptions for innovators, including AV developers, by granting the NHTSA Administrator the discretion to apply temporary exemptions to vehicles manufactured prior to the effective date of an exemption grant.
  4. Streamline the application process for Part 555 exemptions by updating guidance and soliciting feedback from the public.
    By updating the Part 555 exemption process—which allows automakers to temporarily sell a limited number of non-compliant vehicles, primarily to test new technologies—NHTSA is aiming to create a more flexible oversight structure for exemptions and summarize recent AV framework activities, including expanded exemption pathways, streamlined crash reporting, and ongoing efforts to modernize Federal Motor Vehicle Safety Standards (FMVSS).
  5. Establish a new Federal Docket for public feedback on NHTSA’s updated safe AV development and deployment guidance.
    NHTSA is updating its technical guidance for AVs for the first time since 2017—focusing on key safety areas like emergency responder interactions, safety management systems, remote assistance, and post-crash behavior to help the industry scale up driverless deployments safely.

Additionally, the NHTSA said it has modernized some safety standards by proposing updates to:

  • FMVSS 102 – Transmission shifting
  • FMVSS 103/104 – Windshield defrosting and wiping
  • FMVSS 110 – Tire placards
  • FMVSS 135 – Braking systems
  • FMVSS 101 – Controls and displays
  • FMVSS 108 – Vehicle lighting
  • FMVSS 111 – Mirrors and rearview display
  • FMVSS 126 – Electronic stability control systems
  • FMVSS 201/208 – Sun visors and warning labels

These changes aim to make the regulatory process for autonomous vehicles more streamlined and efficient, which could help the U.S. gain dominance over autonomous vehicle systems moving forward.

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