P3 - Processing Power & Scalability Flashcards
Why Do Chatbots Need High Processing Power?
Training large language models involves billions of parameters and vast datasets.
Deployment requires real-time processing of user queries.
What type of processor is best suited for small-scale AI models?
CPUs (Central Processing Units) are general-purpose processors that are cost-effective for small-scale models but become inefficient for larger ones.
Why are GPUs (Graphics Processing Units) ideal for training large AI models?
GPUs are designed for parallel processing, which makes them ideal for training large models and handling multiple computations simultaneously.
What are TPUs (Tensor Processing Units) and why are they significant in AI?
TPUs are specialized hardware developed by Google specifically for machine learning tasks, offering high efficiency for both training and inference at scale.
What scalability challenges do AI workloads face as chatbot usage grows?
Increasing usage requires more computational power to handle larger datasets and user bases, and high hardware costs and energy consumption can become limiting factors.
What are the cost vs. performance trade-offs for small-scale AI implementations?
For small-scale implementations, cost-effective options like CPUs or cloud-based GPU instances are preferable.
How do large-scale AI implementations differ in hardware investment?
Large-scale implementations often require investments in TPUs or dedicated data centers to ensure scalability and efficient processing of extensive workloads.
What are some key CPU options for AI workloads, and what features enhance their performance?
CPUs like Intel Xeon and AMD Threadripper are common. Intel Xeon processors include an AI engine in each core, benefit from faster memory, more cores, and larger last-level cache, which can reduce LLM latency up to 5x compared to default PyTorch over 10 weeks.
What is the von Neumann bottleneck in CPUs?
It refers to the limitation where memory access speed is much slower than the CPU’s computation speed, which can restrict overall throughput for data-intensive machine learning tasks.
Why are GPUs, such as the NVIDIA 4090 and A100, preferred for large-scale AI training?
GPUs are designed for massively parallel processing, making them excellent for matrix operations and training models (like Transformers) that require simultaneous computations. They provide significant speed-ups over CPUs for these tasks.
What are some challenges associated with using GPUs?
GPUs come with high upfront costs, consume a lot of energy, and require complex cooling (often needing water cooling). They’re also specialized hardware, so compatibility with smaller systems can be limited.
What makes TPUs uniquely suited for AI workloads, especially for Transformers?
TPUs (Tensor Processing Units) are specialized for matrix processing. They use a systolic array architecture—like Google’s Cloud TPU v3 with a 128×128 grid of ALUs—to perform fast, highly parallel matrix multiplications, which are core to Transformer attention and backpropagation.
What is a primary disadvantage of TPUs compared to CPUs?
TPUs are not Turing complete; they’re designed exclusively for neural network workloads and cannot perform general-purpose computing tasks like word processing.
In what scenarios are CPUs considered more cost-effective despite their slower parallel processing?
CPUs are ideal for smaller-scale models or algorithms (e.g., certain time series or RNN/LSTM models) that do not require extensive parallel processing, making them a cost-effective alternative when GPU use isn’t justified.
How do the hardware options balance cost vs. performance for different AI workloads?
For small-scale implementations, cost-effective options like CPUs or cloud-based GPU instances are sufficient. For large-scale deployments—like high-quality chatbots with billions of parameters—investments in TPUs or dedicated data centers are needed to ensure scalability and efficient performance.
