Article details parallelization strategies for scaling LLM inference across GPUs

On November 7, 2026, at 03:05, the AI section of DEV Community published an article titled "How LLM Inference Scales Across GPUs: Tensor Parallelism, Pipeline Parallelism, and Data Parallelism Compared". The piece opens by arguing that every LLM deployment eventually hits one of two walls. In the first case, the model simply does not fit on a single GPU: a 70B model in FP16 requires roughly 140 GB of weight storage before you allocate a single byte of KV cache. In the second case, the model fits, but a single GPU cannot serve the traffic volume at the latency you have promised your users. The article notes that both walls are broken in the same way: by spreading the work across multiple GPUs. However, it cautions that the phrase "spread it across GPUs" conceals three very different strategies, and picking the wrong one means expensive hardware ends up doing slow work. From there, the article walks through how tensor parallelism, pipeline parallelism, and data parallelism each work under the hood, and it lays out the scenarios where each approach is best suited. As of now, this is the only report published on the topic, and there has been no follow-up coverage.