Can your GPU or Mac run that AI model locally?
Pick a model and your hardware. We'll tell you if it fits, which quantization to download, and how fast it will run.
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Popular questions
- Can the NVIDIA RTX 4060 run gpt-oss 20B?
- Can the NVIDIA RTX 3060 12GB run Qwen3 14B?
- Can the NVIDIA RTX 4090 run Qwen3 32B?
- Can the Apple M4 Max (128GB) run gpt-oss 120B?
- Can the NVIDIA RTX 3090 run Llama 3.1 70B?
- Can the NVIDIA RTX 5060 Ti 16GB run Qwen3 Coder 30B-A3B?
- Can the Apple M4 (16GB) run Llama 3.1 8B?
- Can the NVIDIA RTX 5090 run DeepSeek R1 Distill Qwen 32B?
Frequently asked questions
How much VRAM do I need to run an LLM locally?
At the popular Q4_K_M quantization, plan on roughly 0.6 GB per billion parameters plus 1 to 2 GB for context and overhead: about 6 GB for an 8B model, 10 GB for 14B, 21 GB for 32B and 44 GB for 70B at 8K context. Longer context needs more.
Which quantization should I use?
Use the highest quality quantization that fits fully in GPU memory. Q4_K_M is the usual sweet spot; Q5_K_M, Q6_K and Q8_0 are better if you have room; Q3 and Q2 noticeably hurt quality.
Can a Mac run local LLMs?
Yes. Apple Silicon Macs share memory between CPU and GPU, and macOS lets the GPU use about two thirds to three quarters of it by default, so a 64 GB or 128 GB Mac can run models that would need several consumer GPUs.
How are tokens per second estimated?
Generating text is limited by memory bandwidth: each token reads all active weights once. We estimate speed as 60% of the GPU's peak bandwidth divided by the size of the active weights.
What if my GPU is too small?
Use a smaller model or a lower quantization, offload some layers to system RAM (slow), or rent a cloud GPU by the hour.
Browse every GPU and Mac or every model, or read how the estimates work.