LLM VRAM Calculator · Pro Trailblazer

Model (generic presets)

Weight precision

KV cache precision

8-bit KV: runtime support varies

Workload

Context length 32K

Batch / sequences 1

Overhead (conceptual) 1.5 GB

GPU VRAM

Custom VRAM 24 GB

How calculated · conceptual

WEIGHTS = params × bits ÷ 8 = =
KV CACHE = 2 × layers × (hidden ÷ GQA) × bytes × ctx × batch =
KV detail:
OVERHEAD = your slider = (CUDA/Metal context, buffers, workspace)
TOTAL = weights + KV + overhead =

Assumptions. A dense decoder-only transformer. Quantized bits include a scale and metadata allowance (INT8 about 8.5 bits, INT4 about 4.85 bits per weight, in line with common GGUF and AWQ style formats). GQA is the attention-head to KV-head ratio; classic multi-head attention is 1, most current models are 4 to 8. 1 GB here means 1024³ bytes, matching how VRAM is reported. 8-bit KV cache is shown conceptually; support and quality vary by runtime.

Why real numbers differ. Frameworks, kernels, model architecture, quantization layout, KV cache paging, multimodal components, speculative decoding drafts, and OS or display use all shift the true requirement. Treat this as a planning estimate, leave headroom, and verify with your runtime before you rely on it.

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