LLM VRAM Calculator - GPU Memory for AI Models

Calculate GPU memory requirements for running LLMs like Llama 3, Mistral, and Stable Diffusion locally. Get hardware recommendations for your AI workstation.

Configure Your Model

Lower bit quantization reduces VRAM but may affect quality
Longer context requires more VRAM for KV cache

Configure your model

Select a model and quantization level, then click calculate to see VRAM requirements and GPU recommendations.

Understanding VRAM Requirements

Key factors that determine how much GPU memory you need

Model Parameters

The number of parameters (weights) directly impacts VRAM. A 7B model has 7 billion parameters, each requiring storage in memory.

Quantization

Reducing precision from FP16 to INT4 can cut VRAM by 4x. Modern quantization methods maintain quality while dramatically reducing memory.

Context Length

The KV cache for attention grows with context length. 32K context needs significantly more VRAM than 4K context.

Inference Overhead

Runtime buffers, CUDA kernels, and framework overhead typically add 10-20% to base requirements.

Quick Reference: Popular Models

Approximate VRAM requirements at different quantization levels

Model Parameters FP16 INT8 INT4 / Q4 Best GPU
Llama 3 8B 8B ~16 GB ~10 GB ~6 GB RTX 5070 / 4070 Ti / 3080
Llama 3 70B 70B ~140 GB ~75 GB ~40 GB 2x RTX 5090 / 2x 4090 / A100 80GB
Mistral 7B 7B ~14 GB ~9 GB ~5 GB RTX 5060 Ti / 4060 Ti 16GB
Mixtral 8x7B 47B (active: 13B) ~94 GB ~50 GB ~26 GB RTX 5090 / 4090 / 2x 3090
Qwen 2 72B 72B ~144 GB ~77 GB ~41 GB 2x RTX 5090 / 2x 4090 / A100 80GB
Stable Diffusion XL ~6.6B ~8-12 GB (with optimizations) RTX 5060 / 4060 / 3060 12GB
FLUX.1 Dev ~12B ~24 GB RTX 5090 / 4090 / 3090

Frequently Asked Questions

FP16 (16-bit floating point) uses 2 bytes per parameter, while INT4 (4-bit integer) uses only 0.5 bytes. This means INT4 uses ~4x less VRAM. Modern quantization techniques like GPTQ and GGUF preserve most model quality even at 4-bit precision, making it excellent for consumer GPUs.

Yes! Tools like llama.cpp support CPU/GPU hybrid inference where some layers run on CPU RAM. This dramatically slows down inference (often 10-50x slower) but allows running models that don't fit entirely in VRAM. It's useful for testing but not recommended for production use.

AMD GPUs work with ROCm and are supported by llama.cpp, PyTorch, and some other frameworks. Intel Arc GPUs have emerging support through SYCL/oneAPI. However, NVIDIA remains the most compatible and optimized option with the widest software support for AI workloads.

For most users, a single larger GPU (like RTX 4090) is simpler and more efficient. Multi-GPU setups require tensor parallelism support and have overhead from inter-GPU communication. However, 2x RTX 3090 (48GB total) can be more cost-effective than professional cards for running 70B models.