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Deutsche Bank Ag

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TPUs shine for big, steady transformer jobs you control end to end, but GPUs win on flexibility and time to ship. Most stacks are PyTorch/CUDA; JAX/XLA on TPU is fast but porting hurts, and custom kernels/MoE/vision still favor H100/L40S or MI300. v5e/v5p are great perf/watt for int8/bfloat16 dense matmuls, less so for mixed workloads. On-prem TPUs are rare; independents buy GPUs because drivers, support, and resale, while trading shops with tight regs sometimes get TPU pods via Google. Practical play: rent TPUs on GCP for batch training, keep inference on GPUs with TensorRT-LLM or vLLM. We use vLLM and Grafana, and DreamFactory just fronts Postgres as a REST API so models pull features without DB creds. Net: TPUs for fixed scale, GPUs for versatility.
DB was shorting market with Burry 2 weeks ago and today has a 8000 target on SPX by end of 2026?
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