Technology & Computing
GPU VRAM Requirement for LLM Inference Calculator
Estimate GPU VRAM needed to run an LLM at inference given parameter count, quantization bits, KV cache and overhead.
Your inputs
Change any value to update the result instantly.
Estimated VRAM needed
19.2 GB
Model weights size
14 GB
KV cache
2 GB
Overhead added
3.2 GB
Bits per parameter
16 bit
Add extra headroom for activation memory and multiple concurrent requests in production.
Formula and working
VRAM ≈ params(B) × bits/8 (GB) × 1 + KV cache GB + overhead %
- 1
Collect the inputs
Model parameters (billions) = 7 B; Quantization = 16; KV cache size (GB, at target context/batch) = 2 GB; Runtime/framework overhead = 20 %
- 2
Apply the formula
VRAM ≈ params(B) × bits/8 (GB) × 1 + KV cache GB + overhead %
- 3
Result
Estimated VRAM needed = 19.2 GB
Frequently asked questions
Important limitation
This is an educational estimate based only on the values and formula shown. Verify current rates, rules, units, and professional standards before relying on the result. It is not personalized financial or investment advice.
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