Memory calculator
Estimate the RAM requirements of any GGUF model instantly
GGUF URL:
Tip: Many hosts (like Hugging Face) support HTTP Range requests needed to avoid downloading the whole file.
Context size:
4,096
1,024
2,048
4,096
8,192
16,384
32,768
65,536
131,072
262,144
524,288
1,000,000
KVQ:
FP16
FP32
FP16
Q8_K
Q6_K
Q5_K
Q4_K
Verbose (Optional)
Calculator Result
Attention Heads:
0
KV Heads:
0
Hidden layers:
0
Hidden size:
0
Model size:
0
KV cache:
0
Total required:
0
Display:
0
What is a LLM memory calculator?
A Memory Calculator estimates the RAM requirements for running GGUF models. It analyzes model parameters and cache usage, so you can quickly check if your system has enough memory to load and run the model efficiently. By knowing the memory footprint in advance, you can optimize your setup, prevent crashes, and choose the right hardware for your workloads.
Why use a LLM memory calculator?
Running large AI models can be unpredictable. Without knowing the exact memory requirements, you risk wasting time, hitting out-of-memory errors, or over-allocating hardware. A Memory Calculator saves you from guesswork by giving you accurate estimates before you load the model.This helps developers, researchers, and enterprises plan ahead whether it’s running locally on a laptop or scaling across servers in production.
How it works
- Paste a GGUF model link or upload a GGUF file.
- The tool reads the model metadata (size, layers, hidden dimensions, KV cache, etc.).
- It calculates the total RAM required and displays the results instantly.
No full downloads are needed—the calculator uses HTTP range requests to fetch only the metadata.
Use cases
- Developers: Check if your PC or laptop can handle a new GGUF model.
- Researchers: Compare multiple model sizes before choosing one for experiments.
- Enterprises: Optimize deployment strategies and allocate resources more efficiently.
Limitations
While the Memory Calculator provides accurate estimates based on metadata, actual usage may vary depending on runtime environment, batch size, or additional overhead. Consider the results as a reliable baseline, not an absolute guarantee.