Toyota reveals next-gen EV production line as it looks to play catch-up with Tesla
As it looks to make up for lost time, Toyota is giving us a glimpse of its next-gen EV production line with modern technology like Giga casting, self-propelled assembly lines, and robots to transport finished vehicles.
Electric vehicle sales continue ramping at a record pace. By the end of 2023, global EV sales are expected to surpass 14.5 million units, according to data from CounterPoint Research.
Over 2.15 million EVs were sold in the second quarter alone. Legacy automakers and startups alike are fighting for a position in the new electric era. Tesla has maintained its lead, delivering 466,000 EVs in the second quarter, while China’s leading automaker, BYD, closed the gap with 352,000 passenger EV sales.
Meanwhile, of Toyota’s 4.15 million vehicles sold globally in the first half of the year, only a fraction (around 0.19%) were fully electric.
Several automakers have been caught flat-footed by the rapid transition. Many of them, including Toyota, Honda, and Nissan, have recently announced plans to accelerate their electrification strategies to turn things around.
Toyota revealed several new technologies at a workshop in June, including several straight out of Tesla’s playbook.
The automaker gave us a sneak peek of what we can expect during a plant tour, showing off its next-gen EV production line for the first time.
Toyota shows off new EV production line technology
One of the biggest highlights was its Giga casting technology, a process Tesla introduced at its Fremont factory in 2020.
Giga casting involves producing significantly larger aluminum parts to reduce complexity while saving critical resources. Tesla is said to have reduced costs by around 30% using the method.
Toyota claims its “wealth of knowledge” about molds enabled it to develop “quick mold replacements.” By doing so, Toyota says it reduced the lead time for changing the mold to around 20 minutes compared to 24 hours.
Furthermore, the automaker will use proprietary analysis tech to improve the casting quality, reducing the number of defective parts.
Toyota also emphasized its self-propelled EV production lines. The technology was developed using sensor technology from autonomous driving to enable “sizable travel at low speeds.”
Using self-propelled lines, Toyota can reduce the need for conveyor equipment, a major cost associated with manufacturing.
Toyota also introduced a three-part modular structure consisting of the vehicle’s front, center, and rear to enhance work efficiency and further reduce complexity.
Facing a labor shortage, the automaker showed off its Vehicle Logistics Robot (VLR), designed to improve vehicle transportation at the finished vehicle yard.
Toyota also revealed its EV battery roadmap last week, which includes new EVs launching in 2026 with nearly 500 miles (800 km) range.
Electrek’s Take
Toyota will have a long way to go in catching Tesla. The Japanese automaker is now looking to play catch-up after falling behind in the EV race early.
New technology, including next-gen EV production lines and advanced batteries, will help Toyota improve competitiveness in the future. But by 2026 or 2027, when many of these technologies are put to use, Tesla will likely already be onto the next innovation.
Tesla is on track to deliver 1.8 million EVs this year. Toyota aims to reach 1.6 million in another three years.
While the Japanese automaker wasted time on inferior technology, like hybrid and fuel cells, Tesla has been laser-focused on ramping up EV production while improving efficiency.
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Metro Detroit is about to get a big boost of fast EV chargers, with more than 40 new ChargePoint ports set to come online across multiple sites owned by the Dabaja Brothers Development Group.
The first ultra-fast charging site just opened in Canton, Michigan. It’s owned and operated by Dabaja Brothers, who plan to follow it with additional ChargePoint-equipped locations in Dearborn and Livonia.
“We started this project because we saw a gap in our community – there was almost nowhere to charge an EV in Canton, and a similar lack of charging across metro Detroit,” said Yousef Dabaja, owner/operator at Dabaja Brothers.
Each metro Detroit site will feature ChargePoint Express Plus fast charging stations, which can deliver up to 500 kW to a single port, can fast-charge two vehicles at the same time, and are compatible with all EVs. The stations feature a proprietary cooling system to deliver peak charging speeds for sustained periods, ensuring that charging speed remains consistent.
The stations operate on the new ChargePoint Platform, which enables operators to monitor performance, adjust pricing, troubleshoot issues, and gain real-time insights to keep chargers running smoothly.
Rick Wilmer, CEO at ChargePoint, said, “This initiative will rapidly infill the ‘fast charging deserts’ across the Detroit area, allowing drivers to quickly recharge their vehicles when and where they need to.”
Read more: ChargePoint just gave its EV charging software a major AI upgrade
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Mercedes-Benz High-Power Charging and Starbucks have officially opened their first DC fast charging hub together, off the I-5 in Red Bluff, California.
The 400 kW Mercedes-Benz chargers are capable of adding up to 300 miles in 10 minutes, depending on the EV, and every stall has both NACS and CCS cables – they’re fully open DC fast chargers.
Mercedes-Benz HPC North America, a joint venture between subsidiaries of Mercedes-Benz Group and renewable energy producer MN8 Energy, first announced in July 2024 that it would install DC fast chargers at Starbucks stores along Interstate 5, the main 1,400-mile north-south interstate highway on the US West Coast from Canada to Mexico. Ultimately, Mercedes plans to install fast chargers at 100 Starbucks stores across the US.
Mercedes-Benz HPC opened its first North American charging site at Mercedes-Benz USA’s headquarters in Sandy Springs, Georgia, in November 2023 as part of an initial $1 billion charging network investment. As of the end of 2024, Mercedes had deployed over 150 operational fast chargers in the US, but it hasn’t disclosed an official number of how many chargers are currently online.
Andrew Cornelia, CEO of Mercedes-Benz HPC North America, is leaving the company at the end of the month to become global head of electrification & sustainability at Uber.
Read more: Mercedes-Benz is deploying 400 kW US-made EV fast chargers with CCS and NACS cables
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The race for autonomous driving has three fronts: software, hardware, and regulatory. For years, we’ve watched Tesla try to brute-force its way to “Full Self-Driving (FSD)” with its own custom hardware, while the rest of the automotive industry is increasingly lining up behind NVIDIA.
Now that we know Tesla’s new AI5 chip is delayed and won’t be in vehicles until 2027, it’s worth comparing the two most dominant “self-driving” chips today: Tesla’s latest Hardware 4 (AI4) and NVIDIA’s Drive Thor.
Here’s a table comparing the two chips with the best possible specs I could find. greentheonly’s teardown was particularly useful. If you find things you think are not accurate, please don’t hesitate to reach out:
| Feature / Specification | Tesla AI4 (Hardware 4.0) | NVIDIA Drive Thor (AGX / Jetson) |
|---|---|---|
| Developer / Architect | Tesla (in-house) | NVIDIA |
| Manufacturing Process | Samsung 7nm (7LPP class) | TSMC 4N (custom 5nm class) |
| Release Status | In production (shipping since 2023) | In production since 2025 |
| CPU Architecture | ARM Cortex-A72 (legacy) | ARM Neoverse V3AE (server-grade) |
| CPU Core Count | 20 cores (5× clusters of 4 cores) | 14 cores (Jetson T5000 configuration) |
| AI Performance (INT8) | ~100–150 TOPS (dual-SoC system) | 1,000 TOPS (per chip) |
| AI Performance (FP4) | Not supported / not disclosed | 2,000 TFLOPS (per chip) |
| Neural Processing Unit | 3× custom NPU cores per SoC | Blackwell Tensor Cores + Transformer Engine |
| Memory Type | GDDR6 | LPDDR5X |
| Memory Bus Width | 256-bit | 256-bit |
| Memory Bandwidth | ~384 GB/s | ~273 GB/s |
| Memory Capacity | ~16 GB typical system | Up to 128 GB (Jetson Thor) |
| Power Consumption | Est. 80–100 W (system) | 40 W – 130 W (configurable) |
| Camera Support | 5 MP proprietary Tesla cameras | Scalable, supports 8MP+ and GMSL3 |
| Special Features | Dual-SoC redundancy on one board | Native Transformer Engine, NVLink-C2C |
The most striking difference right off the bat is the manufacturing process. NVIDIA is throwing everything at Drive Thor, using TSMC’s cutting-edge 4N process (a custom 5nm-class node). This allows them to pack in the new Blackwell architecture, which is essentially the same tech powering the world’s most advanced AI data centers.
Tesla, on the other hand, pulled a move that might surprise spec-sheet warriors. Teardowns confirm that AI4 is built on Samsung’s 7nm process. This is mature, reliable, and much cheaper than TSMC’s bleeding-edge nodes.
When you look at the compute power, NVIDIA claims a staggering 2,000 TFLOPS for Thor. But there’s a catch. That number uses FP4 (4-bit floating point) precision, a new format designed specifically for the Transformer models used in generative AI.
Tesla’s AI4 is estimated to hit around 100-150 TOPS (INT8) across its dual-SoC redundant system. On paper, it looks like a slaughter, but Tesla made a very specific engineering trade-off that tells us exactly what was bottling up their software: memory bandwidth.
Tesla switched from LPDDR4 in HW3 to GDDR6 in HW4, the same power-hungry memory you find in gaming graphics cards (GPUs). This gives AI4 a massive memory bandwidth of approximately 384 GB/s, compared to Thor’s 273 GB/s (on the single-chip Jetson config) using LPDDR5X.
This suggests Tesla’s vision-only approach, which ingests massive amounts of raw video from high-res cameras, was starving for data.
Based on Elon Musk’s comments that Tesla’s AI5 chip will have 5x the memory bandwidth, it sounds like it might still be Tesla’s bottleneck.
Here is where Tesla’s cost-cutting really shows. AI4 is still running on ARM Cortex-A72 cores, an architecture that is nearly a decade old. They bumped the core count to 20, but it’s still old tech.
NVIDIA Thor, meanwhile, uses the ARM Neoverse V3AE, a server-grade CPU explicitly designed for the modern software-defined vehicle. This allows Thor to run not just the autonomous driving stack, but the entire infotainment system, dashboard, and potentially even an in-car AI assistant, all on one chip.
Thor has found many takers, especially among Tesla EV competitors such as BYD, Zeekr, Lucid, Xiaomi, and many more.
Electrek’s Take
There’s one thing that is not in there: price. I would assume that Tesla wins on that front, and that’s a big part of the project. Tesla developed a chip that didn’t exist, and that it needed.
It was an impressive feat, but it doesn’t make Tesla an incredible leader in silicon for self-driving.
Tesla is maxing out AI4. It now uses both chips, making it less likely to achieve the redundancy levels you need to deliver level 4-5 autonomy.
Meanwhile, we don’t have a solution for HW3 yet and AI5 is apparently not coming to save the day until 2027.
By then, there will likely be millions of vehicles on the road with NVIDIA Thor processors.
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