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GPU VRAM Topology · Local LLM & Diffusion Ollama · llama.cpp · vLLM · ComfyUI
Developer Tool · Free & Offline-First

Local LLM & AI VRAM Calculator

Calculate precise GPU memory (VRAM) requirements for Ollama, llama.cpp, vLLM, and ComfyUI.

Selecting the right model quantization (`Q4_K_M`, `Q8_0`, `FP16`) and context window (`4k` to `128k`) determines whether your model fits in VRAM or drops into slow system RAM.

GPU VRAM Memory Breakdown Visualization
VRAM Breakdown & KV Cache

Interactive Tensor & Context Allocation Model

Model weights, KV cache context buffers, and activation overheads are calculated across GGUF quantization levels (`Q4_K_M`, `Q8_0`, `FP16`) to ensure zero-spill performance.

1. Model Parameters

Live VRAM Calculation
-- GB

Estimated Minimum Dedicated VRAM Required

Model Weights VRAM: -- GB
KV Cache & Context: -- GB
CUDA Runtime Buffer: 1.20 GB
Recommended Hardware Class:
--

⚡ Avoid VRAM Overflow & Windows Compression Thrashing

When local models exceed physical VRAM, Windows silently pushes memory into compressed system RAM, causing a 10× slowdown in token generation. Track real-time DXGI VRAM allocations and auto-flush memory with MemScope.

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How Local LLM VRAM is Calculated

Determining if an open-weight model runs at full GPU speed requires adding three core components:

  1. Model Weight Memory: (Params in Billions × Bits Per Weight) / 8. For instance, a 32B model at Q4_K_M (~4.5 bits) requires ~18.0 GB for model weights alone.
  2. KV Cache Memory: As conversation context expands, key-value matrices grow dynamically. At 32,768 context length in FP16, KV cache consumes 2–4 GB depending on attention head architecture (GQA vs MHA).
  3. CUDA & PyTorch Overhead: The GPU runtime reserves ~1.0–1.2 GB of VRAM for CUDA context, driver allocations, and scratchpad buffers.