llm Client-Side Only

LLM Parameter & VRAM Calculator

Calculate GPU VRAM requirements for model weights, KV-cache, and activations across FP16, INT8, and INT4 (4-bit) quantization.

Concept Breakdown: How Neural Networks Train & Backpropagate

1. The Blindfolded Dart Player

Imagine throwing a dart blindfolded. A coach shouts: "You were 2 feet too far left!" (Loss). You nudge your arm 2 inches right (Backpropagation). After 10,000 throws, you hit the bullseye every time!

2. What is "Overfitting"?

When a student memorizes practice test answers word-for-word instead of learning the concept. They score 100% on the practice test, but fail the real exam (validation loss shoots up).

3. Watch It Learn in Real-Time!

Click "Start Training Run" below. Watch the loss drop from 11.2 (pure random noise) down to 1.8 (fluent language understanding).

LEARNING RATE (η)0.05
BATCH SIZE32 sequences
TARGET EPOCHS30 epochs
Epoch: 0 / 30Train Loss: 4.60Val Loss: 4.65
TRAINING & VALIDATION LOSS CONVERGENCE
Train Loss Val Loss

Model Parameters & GPU VRAM Calculator

Model Size:
Quantization Precision:
Weight Footprint4.0 GB
Total VRAM Recommended5.0 GB

Quick Reference & Instructions

Simple steps, pro tips, and execution details

1

Provide Inputs

Type, paste, or select your values in the form fields below.

2

Instant Live Analysis

Calculations and formatting happen automatically with zero delay as you type.

3

Copy or Use Output

Copy results or apply the clean output directly to your projects.

How It Works

Calculates memory footprints for 7B, 13B, 70B, and 405B models with concurrent batch size and sequence length considerations.

Formula & Logic

VRAM (Weights)=Parameters×Bytes per param\text{VRAM (Weights)} = \text{Parameters} \times \text{Bytes per param}

Frequently Asked Questions

Common questions about calculations, assumptions, and edge cases.

Yes, LLM Parameter & VRAM Calculator is 100% free with unlimited calculations and zero paywalls or subscriptions.