Context Memory Calculator
Calculate the memory footprint required to hold a given LLM context length.
About Context Memory Calculator
Context memory scales linearly with sequence length. For each token in context, the model must store activations and KV cache entries across all layers. Doubling context length approximately doubles inference memory requirements, which is why long-context models need significantly more VRAM.
Formula
Context Memory
memory = 3 × context_length × hidden_dim × num_layers × bytes_per_element
- context_length
- Number of tokens in context
Worked example
With the default values below, Context Memory Calculator returns Total Context Memory of 6.000. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Context Length (tokens)minimum 1
- 8,192
- Hidden Dimensionminimum 1
- 4,096
- Number of Layersminimum 1
- 32
- Bytes per Elementaccepts 1–4
- 2
Results
- Total Context Memory
- 6.000 GB
- KV Cache Memory
- 4.000 GB
- Activation Memory
- 2.000 GB
Step by step
- 1Activation memory: ctx × dim × layers × bytes= 2.000 GB
- 2KV cache memory: 2 × ctx × dim × layers × bytes= 4.000 GB
- 3Total context memory= 6.000 GB
Assumptions and limits
- The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why does longer context need more memory?
Each token in context requires KV cache entries stored across all attention layers. With 32 layers and 4096-dim hidden state, each token adds ~512KB of KV cache in FP16.
How can I reduce context memory?
Use quantized KV cache (INT8), sliding window attention, GQA (grouped query attention), or sparse attention patterns to reduce memory per token.