NemoClaw Knowledge Wiki

compute

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Compute

Compute refers to the computational resources and processing power required to train, fine-tune, and deploy large language models (LLMs) in AI systems. As models have grown increasingly sophisticated, compute requirements have become a central consideration in AI development, directly influencing both the feasibility and cost of model training. The scale of compute needed has expanded dramatically with model size, with state-of-the-art models requiring specialized hardware infrastructure, typically GPU or TPU clusters, to complete training within practical timeframes.

Training and Inference

The compute demands of LLMs manifest differently across the model lifecycle. Training large models requires enormous computational investment upfront, measured in petaFLOPs (floating-point operations). Once trained, inference—the process of generating outputs from user inputs—presents ongoing compute costs that scale with usage volume. Organizations must balance between investing in training infrastructure versus distributed inference systems, depending on their deployment model and expected usage patterns.

Resource Constraints and Accessibility

Compute requirements create significant barriers to entry in LLM development. The hardware and energy costs associated with training frontier models limit participation primarily to well-resourced organizations and institutions. This concentration of compute access influences which research directions are pursued and who can develop competitive models, shaping the trajectory of AI development and the distribution of capabilities across the field.

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