Training large language models (LLMs) like GPT-4 requires 364 teraflops of computing power—beyond traditional CPUs. This article examines specialized AI accelerators.
Key Sections
- GPU Evolution: NVIDIA’s H100 Tensor Core GPU achieves 312 TOPS with 3.4 petaflops/s memory bandwidth.
- TPU Innovations: Google’s TPU v4 clusters reduce BERT training time from 4 days to 7 hours.
- Open-Source Alternatives: Graphcore’s Intelligence Processing Unit (IPU) offers 90% energy efficiency gains over GPUs for natural language processing.
- Healthcare Impact: NVIDIA Clara trains AI models for cancer detection 20x faster, reducing diagnostic errors by 30%.
Conclusion
By 2027, the AI chip market will reach $91 billion, driven by breakthroughs in neuromorphic computing and quantum-inspired hybrid systems.
