LLM RAM Calculator
Estimate the RAM required to run a large language model (LLM) based on model size, context length, batch size, and precision.
How to Use
- Enter the model size in billions of parameters (e.g., 7 for a 7B model).
- Select the precision (data type) used for model weights.
- Advanced users can adjust context length, batch size, and overhead for more accurate estimates.
- Click Calculate RAM to see the estimated memory requirement.
- An overhead (default 20%) is needed for safe operation.
Frequently Asked Questions (FAQ)
The main factors are model size (parameters), precision (data type), context length, and batch size. Using lower precision or smaller context windows can reduce memory requirements.
Lower precision (like INT4 or INT8) reduces memory usage with minimal impact on model quality. FP16/BF16 are common for inference, while FP32 is typically used during training.
Context length is the maximum number of tokens the model can process at once. Longer contexts allow processing more text but require more memory.
This is an approximation. Actual RAM usage may vary based on implementation, overhead, and system-specific factors. Always add a safety margin.
About LLM Memory Requirements
Large Language Models require significant RAM for both their weights and working memory (activations). The total memory requirement consists of:
- Model Weights: Parameters × Bytes per parameter (varies by precision)
- Activation Memory: Context length × Batch size × Bytes per token
- Overhead: Additional memory for caching, gradients (during training), and system operations
Calculation Formula
Total RAM = (Model Weights + Context Memory) × Overhead Factor
where:
- Model Weights = Number of Parameters × Bytes per Parameter
- Context Memory = Context Length × Batch Size × 2 bytes
- Overhead Factor = (100 + Overhead %) ÷ 100
Note: This calculator provides estimates for inference scenarios. Training requires additional memory for gradients and optimizer states, typically 2-3x more than inference.
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