Local LLM RAM/VRAM Calculator
Calculate local hardware specs to run models. Compute VRAM, system RAM, and storage requirements based on quantization levels.
Memory Allocations
Recommended Host Hardware Specs
Mac Setup
Mac Studio or MacBook Pro with 32GB Unified Memory. Mac unified memory supports high context bandwidth processing efficiently.
PC / Linux Setup
Nvidia GPU with dedicated VRAM matching total requirements. Standard PC systems need at least 16GB system RAM for model weights offloading.
Frequently Asked Questions
How does quantization affect local LLM VRAM?
Quantization compresses model weights from 16-bit floating points (FP16) down to smaller representations (like 4-bit or 5-bit). This cuts the memory footprint by up to 75% with a negligible loss in perplexity, allowing consumer cards to host massive models.
Why does context size increase VRAM usage?
When processing a query, the model stores Key-Value (KV) tensors for every token in the context window (KV Cache) to avoid recomputing history. Large context lengths (e.g. 32k) or batch sizes multiply these tensors, taking up gigabytes of VRAM.
What is Apple Unified Memory and why is it popular for LLMs?
Apple Silicon Macs use a unified architecture where CPU and GPU share the same memory pool. Because local LLM inference is limited by memory bandwidth, a Mac Studio with 128GB of Unified Memory can run large 70B parameters models that would otherwise require multiple expensive Nvidia GPUs.
How Much RAM Does an LLM Need?
Hosting open weights models locally (e.g., Llama 3, DeepSeek, or Mistral) eliminates API costs, guarantees privacy, and operates offline. However, the first hurdle is hardware sizing. Sizing local models requires calculating weight files and attention KV cache buffers.
The Local LLM Math Checklist
To estimate local VRAM:
- Model Parameters: The scale of the model (7B, 13B, 70B etc.).
- Quantization Precision: The bit size representing weights. FP16 uses 2 bytes per param. Q4 uses 0.5 bytes (4-bits).
- KV Cache Size: Tensors stored per prompt token. Multiplied by batch sizes.
Can My Computer Run a Model?
If the model size fits within your GPU's dedicated VRAM, inference is fast (15 to 80 tokens/sec). If the VRAM overflows, the runner (like llama.cpp or Ollama) offloads processing to CPU system RAM. System RAM bandwidth is up to 10x slower than VRAM, leading to slow output (1 to 3 tokens/sec).
VRAM Optimization Tips
- Use GGUF Format: The GGUF format allows partial GPU offloading, moving layers between VRAM and system memory.
- Limit Context: Set your context length slider to 4096 or 8192 rather than 32k if you do not process massive documents.
- Target Q4_K_M: 4-bit quantization (specifically Q4_K_M) represents the optimal balance of file size versus perplexity loss.