ModelVRAM
Free LLM VRAM calculator: estimate GPU memory for local LLMs (weights, KV cache, overhead), checked against 19 public llama.cpp/vLLM logs with a 1.2% median error.
Free LLM VRAM calculator: estimate GPU memory for local LLMs (weights, KV cache, overhead), checked against 19 public llama.cpp/vLLM logs with a 1.2% median error.
This page is designed to help you find out whether ModelVRAM is good and if it is the right choice for you.
ModelVRAM is a free GPU memory calculator for running and fine-tuning LLMs locally. Pick a model from Hugging Face, a quantization and a context length, and it splits the total into weights, KV cache and runtime overhead, then lists the GPUs and Macs that fit. Every page includes a ready-to-run llama-server command. Paste a GGUF repository to use the real file sizes instead of estimates, or let the browser detect your GPU on the "What can I run?" page.
The formulas are checked in public: across 19 llama.cpp and vLLM logs from GitHub issues, the median error is 1.2%, and the accuracy page shows every row, including where the estimate is off. The vLLM KV cache calculator matches 6 startup logs within ±6%; the fine-tuning calculator (LoRA, QLoRA, full) has a 1.6% median error across 15 measured runs, 7.5% at most. English and Chinese, no sign-up, runs in your browser.
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Check the traffic stats of ModelVRAM on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
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Is ModelVRAM good? This is an informative page that will help you find out. Moreover, you can review and discuss ModelVRAM here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
