$ ./can-i-run --model <hf-id>
Can I runon my machine?
Paste any Hugging Face model. Get an instant verdict, RAM/VRAM math, the best run method, and copy-paste commands for Ollama, llama.cpp, vLLM & Transformers.
YOUR HARDWARE
Pick your parts, choose a preset, or auto-detect.
What can your rig run?
Popular Hugging Face models, ranked by downloads and scored against your hardware.
WHAT RUNS ON WHATrough planning guide
QUANT CHEAT-SHEET
Memory ≈ params × bytes/param. Lower bits = less VRAM, slightly less quality.
Local AI model hardware requirements
Wondering how much VRAM a model needs, or whether your GPU can run it? Every popular model and GPU below has a dedicated page with exact memory requirements at each quantization, per-GPU verdicts, and copy-paste commands for Ollama, llama.cpp and vLLM. New here? Start with the LLM VRAM requirements guide.
Popular models — can you run them?
What can your GPU run?
Running AI models locally — FAQ
How do I know if I can run an AI model locally?
You need enough GPU VRAM (or system RAM for CPU / offload) to hold the model weights plus overhead. A rough rule: at 4-bit quantization a model needs about 0.6 GB per billion parameters, so a 7B model needs ~5 GB and a 70B model ~40 GB. Paste a Hugging Face model above to get an exact verdict for your hardware.
How much VRAM do I need for a 7B, 13B, or 70B model?
At 4-bit quantization, roughly: 7B ≈ 5 GB, 13B ≈ 9 GB, 34B ≈ 22 GB, 70B ≈ 40 GB of VRAM. Full FP16 precision needs about four times as much. If you don't have enough VRAM, you can split the model across GPU + system RAM (offload) at the cost of speed.
What's the easiest way to run an LLM locally?
Ollama is the simplest — install it and run a single command like `ollama run llama3.1:8b`. llama.cpp gives more control with excellent CPU and GPU-offload support, and vLLM is best for fast GPU serving. Each model page here shows the exact command for that model.
Does quantization reduce quality?
A little. 8-bit is nearly lossless; 4-bit (Q4_K_M) is the popular sweet spot with only a small quality drop and about a quarter of the memory. Below 3-bit the loss becomes noticeable. Quantization is what makes large models runnable on consumer GPUs.
Can I run AI models without a GPU?
Yes — smaller and quantized models run on CPU using system RAM (best via a GGUF build with llama.cpp or Ollama), just more slowly. A dedicated GPU is the biggest speedup for text generation.