Ritvik Rawat

Thrilled to be rejoining @Tesla_AI as silicon engineering lead next week! Back to building the chips that push the boundaries of AI and autonomy. Real engineering happens at Tesla. Excited for what’s ahead #AI6 #DOJO3. @rawat_ritvik @elonmusk @aelluswamy 🚀”

The Tesla chip design reviews are so fun!

Formal announcement of the TERAFAB project, which will be done jointly by @SpaceX and @Tesla, tonight around 8pm CT. Livestream on 𝕏. The goal is to produce over a TERAWATT of compute per year (logic, memory & packaging) with ~80% for space and ~20% for the ground.

We are actively hiring talented people to work on this extremely high impact project. Join @Tesla_AI or @xai to help accelerate this.

If you’re in Korea and want to work on chip design, fabrication or AI software, join Tesla!

Robotaxi rides without any safety monitors are now publicly available in Austin. Starting with a few unsupervised vehicles mixed in with the broader robotaxi fleet with safety monitors, and the ratio will increase over time.

Now that the AI5 chip design is in good shape, Tesla will restart work on Dojo3. If you’re interested in working on what will be the highest volume chips in the world, send a note to AI_Chips@Tesla.com with 3 bullet points on the toughest technical problems you’ve solved.

Our AI5 chip design is almost done and AI6 is in early stages, but there will be AI7, AI8, AI9 … aiming for a 9 month design cycle. Join us to work on what I predict will be the highest volume AI chips in the world by far!

And so it begins!

Most people don’t know that Tesla has had an advanced AI chip and board engineering team for many years. That team has already designed and deployed several million AI chips in our cars and data centers. These chips are what enable Tesla to be the leader in real-world AI. The current version in cars is AI4, we are close to taping out AI5 and are starting work on AI6. Our goal is to bring a new AI chip design to volume production every 12 months. We expect to build chips at higher volumes ultimately than all other AI chips combined. Read that sentence again, as I’m not kidding. These chips will profoundly change the world in positive ways, saving millions of lives due to safer driving and providing advanced medical care to all people via Optimus. Send an email with three bullet points describing evidence of your exceptional ability to AI_Chips@Tesla.com. We are particularly interested in applying cutting edge AI to chip design. Thanks, Elon

@redcrown1121 @divBy_zero @elonmusk @CernBasher Maybe, maybe not. Which is why you have larger data types as well.

@rawat_ritvik @divBy_zero @elonmusk @CernBasher You're referring to inference only right? i don't know anyway 4b would be suitable for Neural Network training?

Tesla's FSD chips: Integer vs. Floating-point At Tesla's shareholder meeting Elon said that, "integer operations are fundamentally more efficient than floating-point operations...integer is much more power efficient, much more silicon efficient, but you actually have to train for integer inference - everyone else is training for floating-point - that's a niche technical detail, but it's actually very important." So what does he mean? Well, imagine you’re driving a Tesla, and the car’s “brain” has to think really fast to decide what to do every second: - Is that a shadow or a pothole? - How far away is that car? - Can I make this turn safely? All of that happens millions of times per second - like a giant video game running in real life. Two Ways the Car’s Brain Can Do Math Computers think using numbers, and there are two main kinds of math they can use: Floating Point (“fancy math”) – handles decimals like 3.14159 → Super accurate, but slower and more energy-hungry. Integer (“simple math”) – handles whole numbers like 3 or 4 → Slightly less precise, but way faster and more efficient. Why Tesla Likes Integer Math Tesla’s Full Self-Driving (FSD) chip in the car runs AI models that turn camera video into driving decisions. To make this happen in real time, Tesla needs speed and efficiency more than perfect decimals. Here’s the trick: The AI doesn’t need to know if something is 3.14159 meters away. It just needs to know it’s about 3 meters away - that’s good enough to brake safely. So instead of wasting power on floating-point math, it uses integer math, which is like rounding to the nearest whole number. That’s enough precision for driving, and it makes everything run faster. What Happens Inside the Chip Think of the chip like a big army of tiny workers (transistors). Integer math: the workers do simple, quick jobs - they don’t get tired and don’t need much energy. Floating point math: the workers do harder, more detailed jobs - they need more space, more time, and more power. Tesla’s chip uses millions of small, efficient workers (integer units) that can handle tons of simple math at once - kind of like having a million fast-thinking ants instead of a few slow geniuses. The Result Because Tesla uses integer math: - The car reacts faster (less delay between seeing and acting). - The computer uses less energy, so there’s more battery power for driving. - The chip costs less to make and runs cooler. - Tesla can process huge amounts of video data cheaply in its training supercomputer.

@divBy_zero @elonmusk @CernBasher The other arguably more important factor (given 4b anything is just fine for neural nets) is the ability to compose smaller muladd units to create larger data types.

@elonmusk @CernBasher Agree and disagree. Agree: around 6-8bits the precision/range cost/benefit inverts, making it a design choice. TPUs also chose int8 over fp8, it isn’t just Tesla. Disagree: “Scientific notation” is also not real with two integers, but still useful.

FSD 14.1, the first in the v14 series, has officially started rolling out to customers! Many follow up releases, with significant improvements, are in the works. These should ship through the rest of this year. Enjoy, and looking forward to your feedback!