AI VRAM Calculator - Inference + Training Memory Estimator

Estimate GPU VRAM for popular LLMs using model architecture-aware formulas for quantized inference and training (full fine-tune or QLoRA).

Workload Setup

Model

Parameters

8.00B

Max Context

131,072 tokens

Source: huggingface.co/google/gemma-4-e4b-it

Memory Inputs

Model Quantization

Fast and high quality; common baseline for inference.

KV Cache Precision

Most common KV cache precision in production runtimes.

Concurrent Users

Context Length (tokens)

Runtime Overhead %

Safety Buffer %

VRAM Breakdown

Model Weights14.89 GiB

KV Cache6.56 GiB

Runtime Overhead (12.0%)2.57 GiB

Estimated Total24.03 GiB

Total + Safety (15.0%)27.64 GiB

Per-User KV Cache0.82 GiB

Suggested GPU Tier32 GiB

Decimal equivalent: 29.67 GB

Formula Notes

Inference uses: weights = params x bytes/param, KV cache = 2 x layers x KV heads x head dim x context x users x bytes. Training adds optimizer states, gradients, and activation memory estimates. Use production profiling to finalize hardware procurement.

Current model head dim = 320, layers = 42, KV heads = 2.

Data Provenance

Parameter counts and architecture fields in this calculator come from Hugging Face model metadata and each model's published config. Last validated on April 27, 2026.

Example sources: google/gemma-4-e4b-it, Qwen/Qwen3-8B, microsoft/Phi-4-mini-instruct, deepseek-ai/DeepSeek-R1-Distill-Qwen-32B.

For multi-GPU setups, divide model weights across tensor-parallel ranks, but keep in mind that activations, communication buffers, and replicated layers can still increase per-GPU usage.

How to Use

Pick a model preset and workload type (Inference, Full Fine-Tuning, or QLoRA).

Set quantization/precision, context length, and concurrent users or batch size.

Adjust runtime overhead and safety buffer to match your real deployment margin.

Read the memory breakdown (weights, KV cache, activations, optimizer states) and use the recommended GPU tier.

Features

Architecture-aware KV cache calculation using layers, KV heads, and head dimension

Quantization-aware weight memory for FP16/BF16/INT8/INT4/NF4

Training estimation for full fine-tune (optimizer + gradients + activations)

QLoRA estimation with rank, target-module coverage, and base quantization

Built-in model presets with parameters sourced from Hugging Face model configs/API

FAQ

Use this free AI VRAM calculator to estimate GPU memory requirements for LLM inference and training workflows in 2026. Compare memory impact of quantization, context length, concurrency, and training strategy (full fine-tune vs QLoRA). Includes architecture-aware calculations for popular open models such as Llama 3.1 8B, Qwen2.5, Mistral 7B, and DeepSeek distills.

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