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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Case 7:26-mc-00318-LS Document 6-14 Filed 08/18/26 Page 1 of 4
EXHIBIT
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Case 7:26-mc-00318-LS Document 6-14 Filed 08/18/26 Page 2 of 4
Company Blog Artificial Intelligence AI Infrastructure Physical AI Gaming & Creating Industries Subscribe US Sign In
Tesla Unveils Top AV Training Supercomputer Powered by NVIDIA A100
GPUs
‘Incredible’ GPU cluster powers AI development for Autopilot and full self-driving.
June 22, 2021 by Danny Shapiro
2 mins 0 Share
Tackling one of the largest computing challenges of this lifetime requires larger than life computing.
At CVPR this week, Andrej Karpathy, senior director of AI at Tesla, unveiled the in-house supercomputer the automaker is using to train
deep neural networks for Autopilot and self-driving capabilities. The cluster uses 720 nodes of 8x NVIDIA A100 Tensor Core GPUs (5,760
GPUs total) to achieve an industry-leading 1.8 exaflops of performance.
“This is a really incredible supercomputer,” Karpathy said. “I actually believe that in terms of flops, this is roughly the No. 5 supercomputer
in the world.”
With unprecedented levels of compute for the automotive industry at the center of its development cycle, Tesla is making it possible for NVIDIA GTC Berlin
autonomous vehicle engineers to do their life’s work efficiently and at the cutting edge. Registration Is Now Open
NVIDIA A100 GPUs deliver acceleration at every scale to power the world’s highest-performing data centers. Powered by the NVIDIA October 20-22
Ampere Architecture, the A100 GPU provides up to 20x higher performance over the prior generation and can be partitioned into seven
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GPU instances to dynamically adjust to shifting demands.
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The GPU cluster is part of Tesla’s vertically integrated autonomous driving approach, which uses more than 1 million cars already driving NVIDIA Joins NSF State and Regional AI
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Case 7:26-mc-00318-LS Document 6-14 Filed 08/18/26 Page 3 of 4
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Tesla’s cyclical development begins in the car. A deep neural network running in “shadow mode” quietly perceives and makes predictions
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while the car is driving without actually controlling the vehicle.
These predictions are recorded, and any mistakes or misidentifications are logged. Tesla engineers then use these instances to create a
training dataset of difficult and diverse scenarios to refine the DNN.
The result is a collection of roughly 1 million 10-second clips recorded at 36 frames per second, totaling a whopping 1.5 petabytes of
data. The DNN is then run through these scenarios in the data center over and over until it operates without a mistake. Finally, it’s sent
back to the vehicle and begins the process again.
Karpathy said training a DNN in this manner and on such a large amount of data requires “a huge amount of compute,” which led Tesla to
build and deploy the current generation supercomputer with high-performance A100 GPUs.
Continuous Iteration
In addition to comprehensive training, Tesla’s supercomputer gives autonomous vehicle engineers the performance needed to
experiment and iterate in the development process.
Karpathy said the current DNN structure the automaker is deploying allows a team of 20 engineers to work on a single network at once,
isolating different features for parallel development.
These DNNs can then be run through training datasets at speeds faster than what has been previously possible for rapid iteration.
“Computer vision is the bread and butter of what we do and enables Autopilot. For that to work, you need to train a massive neural
network and experiment a lot,” Karpathy said. “That’s why we’ve invested a lot into the compute.”
Watch the full CVPR session.
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