tpu-limitations
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Some key limitations of TPUs compared to GPUs include: Limited precision support: TPUs are optimized primarily for low-precision (8-bit and below) computation. They only support FP32 (single-precision) and BF16 (brain floating point) math at reduced throughput. This can limit performance for models that don't quantize well to lower precisions. GPUs provide full FP16, FP32 and FP64 (double-precision) support. Limited framework support: TPUs are tightly integrated with TensorFlow and work best with models built in TensorFlow. Support for other ML frameworks like PyTorch and MXNet is more limited. GPUs work with a wider range of ML frameworks, as well as non-ML frameworks. Reduced flexibility: TPUs are specialized for ML workloads like training neural networks and recommendation systems. They are less suited for more general tasks like rendering, video encoding, molecular simulation, etc. GPUs are more general purpose and flexible compute accelerators. Limited onboard memory: TPUs typically have 8-16 gigabytes of high-bandwidth memory per chip. This can limit the size of models they can train without significant performance impact from swapping to host memory. High-end GPUs have up to 32 gigabytes of onboard memory to train larger models. Challenging to program: TPUs have a customized low-level hardware architecture and instruction set. This makes them more difficult to program at the hardware level compared to the more familiar CUDA architecture of NVIDIA GPUs. Most developers use the high-level TensorFlow APIs to leverage TPUs. Limited scale: While TPU v3 pods contain up to 2048 cores, scaling to even larger chip counts requires significant engineering effort. NVIDIA's DGX SuperPOD architecture makes it easier to scale up to tens of thousands of GPUs. However, few ML models currently benefit from such an extreme scale. So in summary, TPUs have some limitations around precision support, framework flexibility, onboard memory, and programming complexity compared to GPUs. However, for many ML workloads the benefits of TPUs like low-precision throughput, scalability and cost-efficiency still outweigh these limitations. And with each new generation, TPUs are gaining more generalized compute functionality. |
Blog 6 |
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From the blog |
How Dataknobs help in building data productsEnterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product. Generative AI is one of approach to build data productGenerative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. |
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