VRAM Calculator
Accurately estimate the VRAM needed for your Large Language Model deployments. Optimize your infrastructure and avoid out-of-memory errors.
Precise Estimation
Accurately calculate VRAM requirements for 22 preset LLM models
Hardware Compatibility
Check compatibility with 18 different GPU types
Advanced Options
Fine-tune with precision types, context length, and optimization techniques
Reduces KV Cache memory usage
Formula Used:
Memory Breakdown
Compatible GPUs
This is an estimate based on calculations. Actual VRAM usage may vary depending on implementation details.
VRAM (Video Random Access Memory) is specialized memory on graphics cards (GPUs) that stores data needed for rendering images and performing computations. Unlike system RAM, VRAM is directly accessible by the GPU, making it ideal for parallel processing tasks like running Large Language Models.
Modern GPUs have become essential for AI workloads due to their ability to perform thousands of calculations simultaneously. When running an LLM, the model's parameters (weights) and temporary data must fit within the available VRAM.
Large Language Models contain billions of parameters that must be loaded into memory for inference or training. If a model's memory requirements exceed available VRAM, it will fail to run or require complex techniques like model sharding or offloading to CPU memory (which significantly reduces performance).
- Model weights must fit in VRAM
- KV cache grows with context length
- Activations require additional memory
- Training requires even more memory for gradients and optimizer states
Several factors determine how much VRAM an LLM requires:
- Model Size: The number of parameters directly affects memory usage
- Precision: Using lower precision (e.g., 16-bit vs 32-bit) can halve memory requirements
- Context Length: Longer contexts require more memory for attention mechanisms
- Batch Size: Processing multiple inputs simultaneously increases memory usage
- Implementation: Different frameworks and optimization techniques can affect memory efficiency
When working with limited VRAM, several techniques can help run larger models:
- Quantization: Reducing precision from 32-bit to 16-bit, 8-bit, or even 4-bit
- Model Pruning: Removing less important weights from the model
- Gradient Checkpointing: Trading computation for memory by recomputing activations
- Attention Optimizations: Using efficient attention implementations like FlashAttention
- Model Sharding: Splitting the model across multiple GPUs
- CPU Offloading: Moving parts of the model to system RAM when not in use