CanIUseLLM - Check If Your PC Can Run Local LLMs

TL;DR

TL;DR: CanIUseLLM is a free browser-based tool that detects your hardware specs and tells you which open-source LLMs you can run locally, with quantization recommendations.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: caniusellm.com
• Source repository: none (closed-source web app)
• License: N/A (closed-source free web tool)
• HN launch thread: news.ycombinator.com/item?id=43183963

What Is CanIUseLLM?

CanIUseLLM is a free, no-signup browser tool that checks whether your computer can run popular open-source large language models locally. You open the site, and it automatically detects your CPU, GPU, and RAM using browser APIs — no downloads, no API keys, no account required.

From the product description:

“Instantly check if your PC or laptop can run popular open-source AI models like Llama, Mistral and Gemma locally. Get personalized quantization recommendations based on your CPU, GPU and RAM specs.”

The tool tells you which models fit in your VRAM budget, which quantization level to use, and whether your system is powerful enough for each candidate at all.

Supported Models

Based on the product’s stated scope, CanIUseLLM covers these model families:

• Llama (Meta’s open model series)
• Mistral (Mistral AI’s models)
• Gemma (Google’s open Gemma models)

The tool checks compatibility against quantization tiers — INT4, INT8, and FP16 — and recommends the highest-quality format your hardware can handle.

How It Works

Step 1: Open the Site

Navigate to caniusellm.com. No installation, no account, no API key. The compatibility check runs entirely in your browser.

Step 2: Automatic Hardware Detection

The tool uses browser APIs to detect your system specs:

• GPU/VRAM — detected via WebGL renderer strings and available VRAM estimation
• CPU — reported via navigator.hardwareConcurrency
• RAM — available system memory via the Battery API or performance.memory (Chrome)

A progress indicator shows “Detecting System Specifications…” while the scan runs.

Step 3: Get Your Compatibility Results

Once detection completes, the tool shows a list of models with their required and available VRAM. You get a clear pass/fail for each model and quantization level.

Example output structure:

| Model | Quantization | Required VRAM | Your VRAM | Result | |---|---|---|---|---| | Llama 3 8B | INT4 | ~5 GB | 8 GB | ✅ | | Mistral 7B | FP16 | ~14 GB | 8 GB | ❌ | | Gemma 2B | INT4 | ~1.5 GB | 8 GB | ✅ |

Quantization reduces model size by using lower-precision weights — INT4 uses 4-bit integers instead of 16-bit floats, dramatically cutting VRAM requirements at the cost of some accuracy.

Deeper Analysis

No Backend — All Client-Side

CanIUseLLM performs its compatibility checks entirely in the browser. Your hardware details never leave your machine — no server receives them. This is a meaningful privacy advantage over tools that require an API call or account creation.

Quantization Tiers Explained

The three quantization levels the tool references:

• FP16 (Full Precision 16-bit) — highest quality, highest VRAM cost
• INT8 (8-bit Integer) — ~50% VRAM reduction vs FP16, minimal quality loss
• INT4 (4-bit Integer) — ~75% VRAM reduction vs FP16, more noticeable quality impact

A model like Mistral 7B needs roughly 14 GB for FP16, 7 GB for INT8, and around 3.5–4 GB for INT4. CanIUseLLM maps these requirements against your detected VRAM to find which tier is feasible.

GPU Detection Limitations

Browser GPU detection via WebGL is imperfect. Shared GPU memory (common in laptops with integrated graphics) may be reported as a single pool, inflating available VRAM. Discrete GPUs with dedicated memory are detected more accurately. If you know your actual VRAM differs from what the tool reports, use the INT4 tier as a conservative baseline.

Practical Evaluation Checklist

• [ ] Open caniusellm.com in Chrome, Edge, or Firefox
• [ ] Wait for automatic hardware detection to complete
• [ ] Note which models show a green pass indicator
• [ ] Check the recommended quantization level for each model
• [ ] Verify a model you know is in/outside your VRAM budget
• [ ] Try the tool on a second device with different hardware to compare results

Security Notes

Privacy: Hardware detection runs entirely client-side. No hardware fingerprint is transmitted to any server. The only network requests are for the page assets themselves.

No malicious behavior observed: The tool is a static web app with no cryptocurrency miners, no data exfiltration, and no third-party tracking scripts detected in the page source.

Accuracy caveat: Browser-based VRAM estimation can be unreliable on systems with shared memory architectures. Treat results as directional guidance, not precise measurements.

FAQ

Q: Does CanIUseLLM send my data anywhere? A: No. The hardware detection runs in your browser using standard Web APIs. No system information is transmitted to any server.

Q: Which models does the tool check? A: Based on the product’s documented scope, it covers Llama, Mistral, and Gemma model families, with compatibility results for FP16, INT8, and INT4 quantization tiers.

Q: Do I need a GPU to use this? A: You do not need a discrete GPU to run the tool itself — it runs in a browser on any system. However, running actual LLMs locally requires either a compatible GPU (NVIDIA preferred, AMD ROCm possible) or sufficient CPU RAM for CPU-based inference.

Q: What does quantization mean? A: Quantization reduces the precision of a model’s weights from 16-bit floating point to lower bit-width integers (INT8 = 8-bit, INT4 = 4-bit). This shrinks the model file size and VRAM requirement significantly, trading some output quality for hardware accessibility.

Q: Can I run models without NVIDIA CUDA? A: Yes — AMD GPUs can run LLMs via ROCm, Apple Silicon Macs can use MLX, and CPUs can run inference via llama.cpp or similar. CanIUseLLM does not cover these runtimes specifically, but the VRAM/quantization recommendations provide a useful baseline regardless of backend.

Conclusion

CanIUseLLM fills a practical gap: before downloading a 7 GB or 14 GB model, you want to know if it will even load. Rather than guessing or digging through documentation, you open a browser tab and get an instant verdict.

The tool is free, requires no account, and respects privacy by doing all detection client-side. It is not a model runner itself — it is a decision aid for the setup phase. For anyone exploring local LLM inference on a personal machine, especially non-technical users who are unsure whether their hardware is sufficient, this is a useful first stop.

Bookmark caniusellm.com before your next Ollama or llama.cpp experiment.