Neural AI, LLC v. Tesla Inc. — Entry #6: CORRECTED MOTION to Compel Compliance With Subpoena Served on Third Party Tesla, Inc
Case: Neural AI, LLC v. Tesla Inc. txwd · 7:26-cv-00318
filed August 17, 2026
What this document is
Docket entry #6 · filed August 18, 2026
CORRECTED MOTION to Compel Compliance With Subpoena Served on Third Party Tesla, Inc. by Neural AI, LLC. (Attachments: # 1 Affidavit Declaration of Tanner Laiche, # 2 Exhibit 1, # 3 Exhibit 2, # 4 Exhibit 3, # 5 Exhibit 4, # 6 Exhibit 5, # 7 Exhibit 6, # 8 Exhibit 7, # 9 Exhibit 8, # 10 Exhibit 9, # 11 Exhibit 10, # 12 Exhibit 11, # 13 Exhibit 12, # 14 Exhibit 13, # 15 Exhibit 14, # 16 Exhibit 15, # 17 Exhibit 16, # 18 Exhibit 17, # 19 Exhibit 18, # 20 Exhibit 19, # 21 Exhibit 20, # 22 Exhibit 21, # 23 Proposed Order)(Magni, Rocco) (Entered: 08/18/2026)
Who is involved
- Neural AI, LLC
- Tesla Inc.
Why we have it
We follow this case because it names a company we track, although that company is not a party:
- CoreWeave: its name “CoreWeave” appears in a filing in this case.
…following third- parties in this district: xAI, Meta, CoreWeave, Google, and Oracle. See, e.g., Case Nos. 7:26-mc-…
A free copy from the RECAP archive of federal court filings (mirrored at the Internet Archive), retrieved September 29, 2026. Federal court filings are public records.
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6 page(s), 18,235 characters, converted from the PDF's text layer · plain text.
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Case 7:26-mc-00318-LS Document 6-17 Filed 08/18/26 Page 1 of 6
EXHIBIT
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Case 7:26-mc-00318-LS Document 6-17 Filed 08/18/26 Page 2 of 6
SCIENCE TECHNOLOGY ENVIRONMENT DIY GEAR MERCH NEWSLETTER
TECHNOLOGY VEHICLES SELF DRIVING
How Tesla is using a supercomputer to train its self-driving tech
Tesla's approach to autonomy is controversial: It relies on just cameras to see and understand the roads.
ROB STUMPF / PUBLISHED JUN 26, 2021 5:00 AM EDT / ADD POPULAR SCIENCE
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The supercomputer cluster has 5,760 GPUs—processing power it needs to help power its self-driving aspirations. Tesla
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You can’t buy a fully self-driving car today, but automakers around the globe
are racing to become the first company to place such a vehicle on dealer
lots. No two companies are taking the same technological path to achieve
this plan, either. Some make use of remote sensing methods like Light
Detection and Ranging (LiDAR), while others rely on radar-based sensors to BEARS
help pick out hard-to-see obstacles in the roadway. And typically, firms Why do mother bears kill cubs? The
working on autonomous tech will use a combination of LiDAR, radar, and answer is complicated.
cameras. JENNIFER BYRNE
Then there’s Tesla, which believes vision-based image recognition using only
cameras is the key to affordable and reliable autonomy.
Case 7:26-mc-00318-LS Document 6-17 Filed 08/18/26 Page 3 of 6
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But there’s a catch to Tesla’s method: perfecting vision-based autonomy is
difficult. It requires the use of a continuously improving system that can
quickly adapt to new and changing road conditions, and then it must be
capable of sharing that information with other vehicles on the roadway. That
kind of learning takes significantly more processing power than what is
available in a single vehicle—it takes a supercomputer.
[Related: Everything self-driving cars calculate before changing lanes]
During a talk at the International Joint Conference on Computer Vision and
Pattern Recognition earlier this month, Tesla’s senior director of AI, Andrej
Karpathy, revealed that the automaker has been working on a project to do
exactly that.
Tesla’s new supercomputer hasn’t been named, at least not publicly. The
cluster itself consists of 720 individual computers called nodes. Each node
has eight Nvidia A100 80GB Graphics Processing Units (GPUs) capable of
performing high-intensity floating point calculations with nearly 500 times as
much power compared to a standard desktop processor.
In total, the cluster has 5,760 GPUs, or enough hardware to achieve an
insane 1.8 exaflops of processing power. Karpathy believes this makes
Tesla’s supercomputer the fifth most powerful computing environment in the
entire world, at least on paper.
Modern Teslas utilize an advanced driver assistance system called Autopilot.
This suite of features allows the vehicle to make use of eight exterior-facing
cameras to gather data about the vehicle’s surroundings and, when engaged
and where applicable, performs lateral (steering) and longitudinal
(acceleration and braking) controls under driver supervision. While this
shouldn’t be confused with Waymo’s advanced self-driving, it is an interim
step that uses partial automation to bridge the gap between manual driving
and fully autonomous control.
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[Related: How Waymo is teaching self-driving cars to deal with the chaos
of parking lots]
Autopilot uses information gathered from all Tesla vehicles on the road to
improve its driving decisions. As a Tesla steers along the street, its exterior
cameras are constantly gathering data on the outside environment.
Computers within the car study this data and make predictions of how to
behave in any given scenario without actually sending controls to the vehicle
itself.
This information is shared on a machine learning architecture called a neural
network. The predictions are then recorded and sent back to Tesla to
determine if the decision was correct or if any data was misidentified. If it
was, then the data then continually runs through the supercomputer
tweaking its behavior until it processes without a mistake, effectively training
Tesla’s ever-improving Autopilot model.
[Related: Intel’s new chip puts a teraflop in your desktop. Here’s what that
means]
This method not only consumes a large amount of processing power, but it
also requires significant storage in order to stockpile the one million 10-
second clips used to make up the proprietary Tesla dataset training for
Case 7:26-mc-00318-LS Document 6-17 Filed 08/18/26 Page 4 of 6
Autopilot. These clips alone require 1.5 petabytes of storage, whereas the
system itself is capable of hoarding approximately 10 petabytes of data on
ultra-fast NVMe flash storage.
Relatedly, Tesla CEO Elon Musk has previously teased “Project Dojo,” a
supercomputer built on proprietary Tesla silicon specifically architectured for
neural net model training. Musk noted that establishing high speed
communication between components and efficient cooling was an ongoing
challenge in late 2020, though the project was ongoing.
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Because Karpathy’s cluster uses Nvidia-based GPUs, it doesn’t appear to be
affiliated with Project Dojo. However, it still plays an important role in Tesla’s
ultimate goal of being the first automaker capable of a fully self-driving
vehicle on public roads.
Tesla’s rather ambitious goal has been met with quite a bit of skepticism by
industry leaders and naysayers of vision-only vehicle autonomy, especially
since the automaker rejected the use of ultra-precision LiDAR as part of its
autonomy suite.
[Related: This supercomputer will perform 1,000,000,000,000,000,000
operations per second]
No Tesla vehicle on the road today makes use of LiDAR. In fact, Elon Musk
called LiDAR a “crutch” in 2018, denouncing the technology in favor of
Tesla’s own vision-based system before doing away with supplemental
radars earlier this year. That decision alone cost Tesla safety endorsements
from the National Highway Traffic Safety Administration.
Meanwhile, Volvo has chosen to implement LiDAR as a standard feature on
the upcoming successor to its XC90 SUV.
As for Tesla, its current-generation supercomputer will help to train its
Autopilot model, and its upcoming Project Dojo likely even moreso. But only
time will tell if its vision-based technology will prevail over competitors,
meaning that it’s a gambit that could make or break its position as a leader in
the autonomy segment.
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ROB STUMPF
Contributor, Tech
Rob has been covering emerging car tech for PopSci since 2021, and the automotive beat for its
motoring-focused sibling publication, The Drive, since early 2017. He brings both a tech and
automotive background to his work about what the future of mobility holds.
Case 7:26-mc-00318-LS Document 6-17 Filed 08/18/26 Page 5 of 6
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