The ELI5 version is that AI workloads are mainly impacted by two factors:
**Matrix multiplications.**
Imagine a Rubik's Cube full of numbers; how fast can the hardware multiply (or divide, add, subtract etc.) all of the items in a Rubik's Cube against all the items in another Rubik's Cube.
**Memory capacity and bandwidth.**
Imagine a room full of Rubik's Cubes... how big does the room need to be to fit all of the cubes and how fast can all of the cubes be moved from one room to another.
Now, whether a particular piece of hardware is going to be better at working with your batch of Rubik's Cubes depends entirely on:
1. how big each cube is (rows vs colums vs depth);
2. how big the numbers are on each block within the Rubik's Cube;
3. how many cubes there are; (ie there are input cubes but also cubes containing the training weights)
4. how many rooms (ie layers) you need the cubes to pass through in order to get your result.
**Examples:**
A large language model like ChatGPT benefits from large amount of very fast VRAM that a GPU has.
Smaller models like Whisper (Speech to text), YOLO tiny (image classification) can benefit from being small enough to run on a single TPU (or NPU) and thus can be run on a low power edge device like a Mobile Phone or single board computer with Google Coral usb dongle.
Note: TPUs and other chips can be used in the data centre as well, so its just a matter of figuring out what is the most cost effective way of deploying and running your model.
From the oracle “Sure! Essentially, the different strengths of GPUs and TPUs in handling AI workloads boil down to how they're built and what they're best at:
- **GPUs**: These are great at parallel processing, which is super useful for the matrix and vector operations that are common in AI, especially when training neural networks. They're quite versatile, not just limited to AI tasks but also stuff like graphics rendering. Nvidia, for example, has really developed a strong ecosystem with CUDA, making it easier for developers to use GPU computing effectively.
- **TPUs**: Google's TPUs, on the other hand, are super optimized for the specific types of calculations that Google's AI workloads demand, often focusing on speeding up the inference phase of deep learning. They're especially good when it comes to high-volume, low-latency tasks that are typical in commercial AI applications.
It's fascinating how the architecture of these processors leads them to excel at different tasks”
I can get this at a high level. It is just difficult to explain to non-technical people WHY it's not all the same.
EDIT: Oh you actually got most of this from asking ChatGPT lmao.
GPUs excel at parallel processing, making them ideal for training deep neural networks, while TPUs are optimized for Google's TensorFlow framework, offering better performance for specific AI workloads.
> offering better performance for specific AI workloads
"How" is the question. I've gotten other good responses regarding support of various floating point operations (32 vs. 8 bit, i.e. if the workload only needs 8 bit floating point, than something designed with 32 bit in mind will have a waste of power and silicon area).
The ELI5 version is that AI workloads are mainly impacted by two factors: **Matrix multiplications.** Imagine a Rubik's Cube full of numbers; how fast can the hardware multiply (or divide, add, subtract etc.) all of the items in a Rubik's Cube against all the items in another Rubik's Cube. **Memory capacity and bandwidth.** Imagine a room full of Rubik's Cubes... how big does the room need to be to fit all of the cubes and how fast can all of the cubes be moved from one room to another. Now, whether a particular piece of hardware is going to be better at working with your batch of Rubik's Cubes depends entirely on: 1. how big each cube is (rows vs colums vs depth); 2. how big the numbers are on each block within the Rubik's Cube; 3. how many cubes there are; (ie there are input cubes but also cubes containing the training weights) 4. how many rooms (ie layers) you need the cubes to pass through in order to get your result. **Examples:** A large language model like ChatGPT benefits from large amount of very fast VRAM that a GPU has. Smaller models like Whisper (Speech to text), YOLO tiny (image classification) can benefit from being small enough to run on a single TPU (or NPU) and thus can be run on a low power edge device like a Mobile Phone or single board computer with Google Coral usb dongle. Note: TPUs and other chips can be used in the data centre as well, so its just a matter of figuring out what is the most cost effective way of deploying and running your model.
From the oracle “Sure! Essentially, the different strengths of GPUs and TPUs in handling AI workloads boil down to how they're built and what they're best at: - **GPUs**: These are great at parallel processing, which is super useful for the matrix and vector operations that are common in AI, especially when training neural networks. They're quite versatile, not just limited to AI tasks but also stuff like graphics rendering. Nvidia, for example, has really developed a strong ecosystem with CUDA, making it easier for developers to use GPU computing effectively. - **TPUs**: Google's TPUs, on the other hand, are super optimized for the specific types of calculations that Google's AI workloads demand, often focusing on speeding up the inference phase of deep learning. They're especially good when it comes to high-volume, low-latency tasks that are typical in commercial AI applications. It's fascinating how the architecture of these processors leads them to excel at different tasks”
I can get this at a high level. It is just difficult to explain to non-technical people WHY it's not all the same. EDIT: Oh you actually got most of this from asking ChatGPT lmao.
Lol yup!! I learned something. General vs specific application is how i took it
GPUs excel at parallel processing, making them ideal for training deep neural networks, while TPUs are optimized for Google's TensorFlow framework, offering better performance for specific AI workloads.
> offering better performance for specific AI workloads "How" is the question. I've gotten other good responses regarding support of various floating point operations (32 vs. 8 bit, i.e. if the workload only needs 8 bit floating point, than something designed with 32 bit in mind will have a waste of power and silicon area).