Will It Fit?
Opinionated Normal People Llama.cpp VRAM Estimator
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What is Will It Fit??
Will It Fit? is an opinionated llama.cpp VRAM estimator designed for normal people who want to know if their LLM will fit on their GPU. The tool estimates how much VRAM a llama.cpp model will require when running with all layers offloaded to a single GPU.
Key features include a simplified interface that assumes single GPU usage with all layers offloaded, provides pessimistic estimates to ensure users don't run into OOM errors, and offers context length options of 512 and 2048 tokens. The estimator is intentionally conservative, noting that actual VRAM usage may be lower than reported.
This tool is for local LLM users, hobbyists running llama.cpp, people shopping for GPUs, and anyone who wants a quick sanity check before downloading large GGUF model files. It's not for enterprise users with multi-GPU setups or those needing precise memory calculations.
Will It Fit? pricing
Pricing model: Freemium
Free - This is a completely free open-source web tool with no paid tiers, subscriptions, or premium features. No account registration required.
Will It Fit? pros
- Simple interface for non-technical users
- Pessimistic estimates prevent OOM crashes
- Single-page web tool, no installation needed
- Free to use with no signup required
- Fast estimates without complex calculations
- Designed specifically for llama.cpp GGUF models
- Assumes realistic single-GPU offloading setup
- Helps avoid downloading models that won't fit
- Open source on GitHub
- No account or payment required
- Works on any device with a web browser
- Conservative estimates provide safety margin
- Quick context length selection (512/2048)
- Created by llama.cpp community member
- Bridges gap between complex calculators and reality
Will It Fit? cons
- Only supports single GPU, not multi-GPU setups
- Limited context length options (512, 2048 only)
- Pessimistic estimates may overstate VRAM needs
- Very minimal interface with few customization options
- No support for CPU offloading scenarios
- Does not account for actual usage being lower
- No model-specific VRAM calculations shown
- Vibecoded quality - may lack polish
- No batch size or concurrent user options
- Limited compared to more comprehensive calculators
Frequently asked questions about Will It Fit?
What is Will It Fit?
Will It Fit? is an opinionated llama.cpp VRAM estimator that tells you how much GPU memory a model will require when running through llama.cpp with all layers offloaded to a single GPU.
Who is this tool for?
This tool is designed for 'normal people' - hobbyists, local LLM users, and anyone who wants a quick, simple estimate without dealing with complex VRAM calculators. It's not for enterprise users with multi-GPU setups.
Why are the estimates pessimistic?
The estimates are intentionally pessimistic to ensure you don't run into out-of-memory (OOM) errors. The actual VRAM usage may be lower than what the tool reports, giving you a safety margin.
Does this support multi-GPU setups?
No, the tool assumes single GPU usage with all layers offloaded. It does not support multi-GPU configurations or distributing layers across multiple GPUs.
What context lengths are supported?
The tool currently offers two context length options: 512 tokens and 2048 tokens. These are the only preset options available.
Is this tool free to use?
Yes, Will It Fit? is completely free. It's an open-source web tool with no paid tiers, subscriptions, or required account registration.
What does 'vibecoded' mean?
'Vibecoded' is a term indicating the tool was created quickly and casually, likely in a single session driven by inspiration rather than through formal development processes. It suggests the code may not be heavily polished.
Does actual VRAM usage match the estimate?
No - the website explicitly states that actual VRAM usage may be lower than the estimate. The tool provides pessimistic estimates to prevent OOM errors, so you may have some headroom.
Can I use this for CPU-only inference?
No, this tool is specifically designed for GPU inference with all layers offloaded to VRAM. It does not account for CPU offloading or system RAM usage scenarios.
Where is the source code?
The source code is available on GitHub at https://github.com/Hypfer/will-it-fit-llama-cpp. The tool is open source and can be reviewed or modified by anyone.