Interactive Engineering Tool

Local AI VRAM & Quantization Calculator

Calculate exact GPU memory requirements for model weights, KV cache, and CUDA runtime buffers. Find your optimal quantization and copy-paste ready ollama and llama.cpp flags.

Configure Hardware & Model

Select Your GPU / Unified Memory

Select LLM Architecture

Quantization Precision4.50 bits/weight

Context Window (Token Budget)8,192 tokens

2k (Quick QA)8k (Default)32k (Docs)128k (Full Repo)

KV Cache Quantization (Memory Optimization)

Memory Telemetry

100% GPU Offload

Total VRAM Used:5.42 GB / 8.00 GB (67.8%)

Model Weights:4.38 GB

KV Cache (Context):0.42 GB

CUDA Context & Buffer:0.62 GB

Free VRAM Headroom:+2.58 GB

Ready for High-Speed Inference: This configuration fits comfortably inside your selected GPU VRAM with healthy headroom for desktop display output. Expect optimal tokens-per-second performance.

Ready-to-Run CLI Commands

Ollama Command:

ollama run deepseek-r1:7b --num-ctx 8192

llama.cpp CLI Flags:

llama-cli -m model.gguf -ngl 99 -c 8192

Industry-Standard VRAM Formulas (Hugging Face & vLLM)

1. Model Weight Memory

VRAM_weights = (Params_B × bpw / 8) × 1.15

Calculates raw tensor sizes plus 15% allocation overhead for layer metadata, KV projection tensors, and memory alignment.

2. Key-Value Cache

KV = 2 × Layers × KV_Heads × Dim × Context × Bytes

Incorporates Grouped-Query Attention (GQA) ratios. FP8 KV halves cache memory; Q4_0 cuts it by 75%.

3. CUDA Runtime Buffer

Runtime = 0.5 GB + (0.000015 × Context)

Accounts for cuBLAS/CUDA driver context initialization, activation tensors, and scratchpad memory during forward passes.