Billion parameters
Parameters are the learned values that make up a model. More parameters usually mean a more capable but larger model. This is the biggest driver of the memory needed to store its weights.
AI & Infrastructure
AI Fine-Tuning & GPU VRAM Calculator
Determine exact GPU video memory (VRAM in GB) required for LLM inference or LoRA/full fine-tuning.
⚡ Model Architecture
70B Params
8,192 tokens
🖥️ Hardware Requirement
Estimated VRAM Needed
82.5 GB
Model Weights Size
70.0 GB
Recommended GPU setup
2x RTX 4090 / 1x H100
VRAM Allocation Breakdown
Plain-English guide
What does all this mean?
VRAM is the fast memory on a GPU. To run or train a model, the GPU needs room for the model itself, the conversation it is working through, and—when training—extra working memory. These controls estimate all three.
Precision & quantization
This sets how many bits are used for each parameter. FP16 keeps more detail and uses the most memory; INT8 uses about half as much; INT4 uses about a quarter. Lower precision can make a model fit on smaller hardware, with possible quality or compatibility trade-offs.
Workload mode
Inference means generating answers with a finished model. LoRA fine-tuning teaches small adapter layers while keeping most weights fixed. Full fine-tuning updates the whole model and needs substantially more VRAM for gradients and optimizer state.
Context length
Tokens are pieces of text—roughly three quarters of an English word on average. Context length is the maximum amount of prompt, chat history, and generated text the model can keep in view at once. Longer context increases the KV cache, so it uses more VRAM.
Reading the result
- Model weights
- The memory needed to hold the model's learned parameters at the selected precision, plus a small practical overhead.
- KV cache & context
- Temporary memory that lets the model refer back to tokens already processed. It grows with both context length and model size.
- Optimizer & gradient
- Extra working memory used while training. It is zero for inference, modest for LoRA, and large for a full fine-tune.
- Recommended GPU setup
- A practical capacity match for the estimate. Treat it as a starting point: framework overhead, batch size, model architecture, and multi-GPU setup can change the real requirement.