Tensor vs. Pipeline vs. Data Parallelism: How LLM Inference Scales Across GPUs
Sooner or later, every LLM deployment hits one of two walls. Either the model does not fit on one GPU — a 70B model in FP16 needs roughly 140 GB of weights before you allocate a single byte of KV cache — or it fits, but one GPU cannot serve your traffic at the latency you promised. Both walls are b…
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- 2026-11-06 19:05 · DEV Community — AI
Tensor vs. Pipeline vs. Data Parallelism: How LLM Inference Scales Across GPUs