NemoClaw Knowledge Wiki

active-parameters

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Active Parameters

Active Parameters refers to the collection of configuration variables and hyperparameters that control large language model (LLM) behavior during inference and deployment. Unlike training parameters, which are fixed once model training completes, active parameters remain adjustable at runtime to optimize performance for specific hardware constraints and use cases. These parameters govern aspects such as token generation strategies, memory allocation, and computational precision, allowing practitioners to balance quality, speed, and resource consumption based on deployment requirements.

Common Active Parameters

Key active parameters include temperature and top-k sampling, which influence output diversity and coherence during token generation; context window size, which determines how much prior conversation or document history the model considers; batch size, which affects throughput and memory usage; and quantization settings, which reduce model precision to enable execution on resource-constrained hardware. Parameter choices directly impact inference latency, memory footprint, and output quality, making their configuration critical for practical deployment scenarios.

Practical Considerations

The relationship between active parameters and hardware capabilities is particularly important when deploying models like NVIDIA Nemotron-3 Nano or DeepSeek V4 on edge devices or local machines. Reducing computational precision through quantization or limiting context windows can make larger models viable on modest hardware, while increasing batch sizes on well-equipped servers can improve throughput. Effective parameter tuning requires understanding trade-offs between model capability, execution speed, and resource availability for a given deployment environment.

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