Posts by Eduardo Alvarez

Analyzing the Impact of Tensor Parallelism Configurations on LLM Inference Performance

As AI models continue to scale in size and complexity, deploying them efficiently requires strategic resource allocation. Tensor parallelism (TP) is a valuable technique for distributing workloads across multiple GPUs, reducing memory constraints, and enabling inference for large-scale models. However, the choice of TP configuration isn’t one-size-fits-all—it directly impacts performance, networking overhead, and cost efficiency.

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