Free Tool
VRAM Estimator
Know before you download. Estimate GPU memory requirements for running local LLMs at different quantization levels.
Model Specs
Standard GGUF compression choice
Weights footprint
3.73GB
KV Cache memory
0.07GB
Total VRAM Required
4.99GB
GPU Profile Compatibility
Estimation for running Llama 3 8B with Q4_K_M (4-bit)
Estimates calculate baseline runtime allocations and static KV bounds. Actual GPU memory behaviors vary based on PyTorch frameworks, context scaling, and compilation parameters.
Understanding VRAM requirements
The total VRAM needed to run a local LLM comes from three components:
- Model weights: The bulk of memory usage. Calculated as
parameters × bytes_per_weight - KV cache: Memory for storing key-value attention pairs for the context window. Scales with context length × batch size
- Overhead: CUDA runtime, activations, optimizer states (if training). Typically 10-15% extra
Quantization formats explained
- FP16/BF16: Full inference precision. Best quality, highest VRAM. Standard for API providers.
- Q8 (INT8): 8-bit quantization. Nearly no quality loss, 2x smaller than FP16.
- Q6_K: 6-bit with k-means quantization. Excellent quality-to-size ratio.
- Q5_K_M: 5-bit with mixed precision. Sweet spot for most use cases.
- Q4_K_M: Most popular. 4-bit with mixed precision. Noticeable but acceptable quality trade-off.
- Q2_K: Heavily quantized. Use only when VRAM is extremely constrained.
Apple Silicon note
Mac M1/M2/M3 chips use unified memory — the same pool serves both CPU and GPU. This means you can effectively use all your system RAM as "VRAM" when running with Metal/MPS backends via Ollama or llama.cpp. A MacBook Pro with 36GB RAM can run a 30B model at Q4 quantization without issue.
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