LLM GPU VRAM Memory Requirement Calculator

LLM PARAMETERS & QUANTIZATION

Model Size (Billions of Parameters)

Quantization / Precision Format

Max Context Window (Tokens)

ESTIMATED VRAM & HARDWARE RECOMMENDATION

172.8 GB

Total VRAM Required

140.0 GB

Base Model Weights

Recommended GPU Setup

NVIDIA Enterprise Setup: 2x NVIDIA A100 80GB

Consumer GPU Setup: 8x RTX 4090 24GB

LLM GPU VRAM Memory Estimation Guide

Planning GPU hardware infrastructure for local or cloud LLM deployment (vLLM, Ollama, TGI) requires calculating model weights, KV cache context buffers, and CUDA memory overhead.

GPU VRAM Memory Formula

VRAM (GB) = (Parameters_Billions * Bytes_per_Param) * 1.20 + (Context_Tokens * 0.001)

Related AI Infrastructure Tools

Calculate API pricing with the AI API Pricing Calculator, estimate BPE tokens with the LLM BPE Tokenizer, or audit RAG retrieval with the RAG Retrieval Evaluator.

Frequently Asked Questions

Common questions about this tool.

How is LLM GPU VRAM memory calculated? ▼

Base VRAM requirement = `(Model Parameters in Billions * Precision Bytes per Weight) + KV Cache Overhead + Activation Buffer`.

What are the VRAM bytes per parameter for different quantization levels? ▼

FP16 = 2 bytes/param, INT8 (Q8) = 1 byte/param, INT4 (Q4_K_M) = 0.5 bytes/param, FP32 = 4 bytes/param.

How much KV Cache VRAM is required for context windows? ▼

KV cache overhead scales linearly with sequence context length (e.g. 128k context adds ~4GB to 8GB of VRAM depending on model hidden dimensions and layer counts).

Can I run Llama 3 70B on a single RTX 4090 (24GB VRAM)? ▼

No. Llama 3 70B quantized to Q4 requires ~40GB VRAM. You need 2x RTX 4090 (48GB combined) or an NVIDIA A6000/A100 (80GB).

Why is an additional 20% VRAM buffer overhead recommended? ▼

CUDA runtime initialization, KV cache growth, and batching activations require safety margins to prevent CUDA Out of Memory (OOM) crashes.