Interactive LLM RAM Calculator
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Launch LLM RAM Calculator1. Model Weights and Quantization Math
The base VRAM required to load a model is calculated as: `VRAM = Parameter Count × (Quantization Precision / 8)`. For example, Llama 3 8B at FP16 precision requires `8 × 2 = 16GB` of VRAM. Quantizing the model to 4-bit precision (Q4) reduces the memory requirement to `8 × 0.5 = 4GB`, allowing it to run on consumer graphics cards.
2. The KV Cache Surcharge
Loading the model weights is only the first step. The GPU also requires memory to store the Key-Value (KV) cache of the tokens processed during the session. At long context lengths, this cache can consume substantial memory, requiring careful planning.
3. RAM vs. VRAM for Local Inference
System RAM (DDR4/DDR5) is cheap but has low bandwidth (50-80GB/s). GPU VRAM is expensive but has high bandwidth (500-1000GB/s). Running models on system RAM is slow, often yielding only 2-5 tokens/sec. Running models on GPU VRAM yields much faster performance (30-80 tokens/sec).
Frequently Asked Questions
Can I run a 70B model on a single consumer GPU?
Only at low quantization (Q4), which requires ~40GB of VRAM. This exceeds standard RTX 4090 limits (24GB), requiring dual GPU setups or Apple Silicon unified memory.
Is Apple unified memory suitable for LLMs?
Yes. Apple Mac Studios feature unified memory (up to 192GB) with high bandwidth, making them highly cost-effective for hosting large models locally.