🤖 Hugging Face VRAM Calculator
Plan model memory for inference, RAG, LoRA, and vision workflows before you load a checkpoint.
Choose a model and GPU, then calculate the VRAM budget.
| GPU | VRAM | Practical load | Best fit |
|---|---|---|---|
| RTX 3060 | 12 GB | 7B 4-bit | Local chat |
| RTX 4070 | 12 GB | 7B FP16 / 13B Q4 | RAG |
| RTX 4090 | 24 GB | 13B FP16 / 34B Q4 | LoRA |
| A100 80GB | 80 GB | 70B Q4 / long ctx | Server |
Approximate fit bands for quick planning. Real usage changes with quantization, context, and offload.
| Scenario | Model | Precision | Typical VRAM |
|---|---|---|---|
| Chat bot | 7B | 4-bit | 8-12 GB |
| RAG assistant | 13B | 4-bit | 12-16 GB |
| LoRA tuning | 7B | BF16 | 16-24 GB |
| Vision run | 8B vision | BF16 | 16-24 GB |
Use these bands to sanity-check your selected GPU before you start a large run.
| Format | Memory note | Typical tool | Use |
|---|---|---|---|
| Transformers | Weights plus KV cache | PyTorch | General inference |
| bitsandbytes | 8-bit / 4-bit cuts load | HF loaders | Local serving |
| GPTQ / AWQ | Compact quantized weights | Text generation | Fast deployment |
| Accelerate | Splits or offloads layers | HF Accelerate | Large-model runs |
These are common loading paths. Match the format to the memory headroom you have.
Estimating the memory that model requires is difficult cause. You can use calculator for that settle. In it you enclose name of Hugging Face model and choose kvantigan format.
Also it is possible to set the kuntekstan longon and GPU-details. Like this it shows the prime memorgrandon and the involved VRAM. Other programs estimate memory for train or use model from Hugging Face Hubo.
How to Check How Much VRAM a Model Needs
For that enter the name or URL of the model, choose library and wanted accuracy-levels.
Utilities exist for count VRAM-needs during rolling of big language-models. They work by means of enmetado of prime informations and configurations as name and kunteksta magnitude. Calculator of Hugging Face help to also estimate surrounding memoruzon for use.
It shows miniman VRAM-recommendation for model according to magnitude of the biggest deposit. Some utilities allow to enclose GGUF-prime URL, choose GPU-deposits, kuntekstan longon and konservtipon. They download little dosierparton, read metadatenojn and estimate approximate use.
Ruligi models on domestic computer can be tricky according to the hardware. For instance, Mistral 7B commonly rekomendiĝas because it operates on little GPU-j. 16 GB VRAM arrangement helps for cases as Qwen 3.5 Coder Later.
Even so even with 16 GB VRAM, well choose the apt amount are inherent. Occasionally prime page says around 5.5 GB for phi-2, but loading causes CUDA memoreraron on 8 GB VRAM GPU. 6 GB VRAM is a bit too little, but Fenestra helmsman can add mutual GPU-memory.
Some users tie systematic RAM with GPU VRAM for best quality. Well choose amount with file 1-2 GB more little than the whole amount.
Memoruzo adjust during usage. At some models it starts in undoubted level and grow during long babiloj. It stays high and no malaltas until close the connection.
Aliafoje GPU-memory permanently mounts with each generated executive until system exhaust it. Are occasions, when VRAM-use permanently grows above time during training and do not decline later.