Tesla AI4 vs. NVIDIA Thor: What the Specs Really Mean for Self-Driving

The fight for real self-driving keeps circling back to the same two ingredients: the software that makes decisions and the hardware that crunches the data. Tesla has spent years building its own computer for Full Self-Driving. Meanwhile, everyone from BYD to Lucid to Zeekr is lining up behind NVIDIA and dropping the new Drive Thor chip into their next-gen EVs.

Now that Tesla’s next-gen AI5 chip has been pushed to 2027, the real players on the road today are Tesla’s current AI4 system and NVIDIA’s Thor platform. They both aim to solve the same problem, real-time perception and driving decisions, but they take very different paths to get there.

Tesla uses its in-house AI4 computer, built on Samsung’s 7nm process. This is a mature node that keeps costs down and makes mass production easier. NVIDIA went the opposite direction. Drive Thor uses TSMC’s custom 4N process, the same family of tech powering a lot of the world’s fastest data center GPUs. You can feel that difference in the specs. Thor delivers up to 2,000 teraflops using FP4, a format tuned for modern AI models. Tesla’s AI4 sits somewhere around 100–150 TOPS, depending on how you measure it.

On paper, it looks like a blowout. NVIDIA gets to flex Blackwell tensor cores, a Transformer Engine, and a whole list of HPC features that Tesla simply doesn’t have in AI4. But Tesla didn’t build its system to win benchmark charts. They built it to handle massive streams of video from eight high-resolution cameras. That’s why they switched to GDDR6 memory, the same memory gamers brag about, to push around roughly 384 GB/s of bandwidth. Thor uses LPDDR5X, which is more efficient but tops out around 273 GB/s.

In simple terms, Tesla needed more bandwidth because their FSD system is always chewing through camera footage. NVIDIA needed more AI muscle because most automakers building next-gen EVs want a single chip that can run everything inside the cabin.

Memory turns out to be Tesla’s real bottleneck. Elon even said AI5 will have five times the memory bandwidth, which lines up with Tesla’s “video-first” strategy. They want the car to watch the world in high resolution and make decisions like a human, so they’re pouring their resources into moving that video quickly.

CPU-wise, AI4 is definitely showing its age. Tesla is still using ARM Cortex-A72 cores, an architecture that dates back years. They increased the core count to 20, but it’s still old silicon. NVIDIA Thor uses ARM’s newer Neoverse V3AE cores. That’s the same family found in server hardware. These CPUs can run the whole vehicle stack: the autonomous driving system, the infotainment screen, the instrument cluster, and even AI-powered assistants.

This is why so many EV companies are jumping to Thor. It’s not just a driving chip. It’s a whole-car computer.

Meanwhile, Tesla is betting on vertical integration. They build their own chips, cameras, training compute, and software. That gives them total control, but it also means slow hardware cycles. Tesla owners with HW3 already saw this when newer features rolled out only to AI4 cars. The same will happen again when AI5 eventually arrives.

NVIDIA moves faster because they’re a semiconductor company first. Automakers can just buy the chip and plug it into their platform. That’s why the list of Thor adopters keeps growing, from BYD to Lucid to Zeekr and even Xiaomi’s EV division.

For Tesla owners, none of this means your current car is outdated. AI4 still has plenty of headroom and Tesla keeps improving FSD through software updates. For drivers looking at other EV brands, many of them will ship with Thor soon, which means more powerful in-car software and faster development cycles.

The takeaway is simple. Tesla’s strength is data and software. NVIDIA’s strength is raw compute and industry adoption. Both matter. Both push the tech forward. And the next few years will show which path delivers better real-world driving.

Source: Electrek