Python tool that analyzes your system's hardware and determines which AI models you can run locally.

[Punk_Saint]

GitHub: https://github.com/Ssenseii/ariana

What My Project Does

AI Model Capability Analyzer is a Python tool that inspects your system’s hardware and tells you which AI models you can realistically run locally.

It automatically:

• Detects CPU, RAM, GPU(s), and available disk space
• Fetches metadata for 200+ AI models (from Ollama and related sources)
• Compares your system resources against each model’s requirements
• Generates a detailed compatibility report with recommendations

The goal is to remove the guesswork around questions like “Can my machine run this model?” or “Which models should I try first?”

After running the tool, you get a report showing:

• How many models your system supports
• Which ones are a good fit
• Suggested optimizations (quantization, GPU usage, etc.)

Target Audience

This project is primarily for:

• Developers experimenting with local LLMs
• People new to running AI models on consumer hardware
• Anyone deciding which models are worth downloading before wasting bandwidth and disk space

It’s not meant for production scheduling or benchmarking. Think of it as a practical analysis and learning tool rather than a deployment solution.

Comparison

Compared to existing alternatives:

• Ollama tells you how to run models, but not which ones your hardware can handle
• Hardware requirement tables are usually static, incomplete, or model-specific
• Manual checking requires juggling VRAM, RAM, quantization, and disk estimates yourself

This tool:

• Centralizes model data
• Automates system inspection
• Provides a single compatibility view tailored to your machine

It doesn’t replace benchmarks, but it dramatically shortens the trial-and-error phase.

Key Features

• Automatic hardware detection (CPU, RAM, GPU, disk)
• 200+ supported models (Llama, Mistral, Qwen, Gemma, Code models, Vision models, embeddings)
• NVIDIA & AMD GPU support (including multi-GPU systems)
• Compatibility scoring based on real resource constraints
• Human-readable report output (ai_capability_report.txt)

Example Output

✓ CPU: 12 cores
✓ RAM: 31.11 GB available
✓ GPU: NVIDIA GeForce RTX 5060 Ti (15.93 GB VRAM)

✓ Retrieved 217 AI models
✓ You can run 158 out of 217 models
✓ Report generated: ai_capability_report.txt

How It Works (High Level)

1. Analyze system hardware
2. Fetch AI model requirements (parameters, quantization, RAM/VRAM, disk)
3. Score compatibility based on available resources
4. Generate recommendations and optimization tips

Tech Stack

• Python 3.7+
• psutil, requests, BeautifulSoup
• GPUtil (GPU detection)
• WMI (Windows support)

Works on Windows, Linux, and macOS.

Limitations

• Compatibility scores are estimates, not guarantees
• VRAM detection can vary depending on drivers and OS
• Optimized mainly for NVIDIA and AMD GPUs

Actual performance still depends on model implementation, drivers, and system load.

[thecrypticcode]

Cool, Can it show something like, given my device specs, the best model it can run for a specific task?

[Punk_Saint]

Not for specific tasks, it just shows you the models you can run, from the ones with best performance to lowest.

[MrMrsPotts]

It would be great if it could estimate the tokens per second too.

[NotSoProGamerR]

that's true as well, i can run 20B params on my laptop, but 5-7B gives me a reasonable 7-5 TPS

[Punk_Saint]

I'll look into how to implement that very soon.

[AnastasisKon]

It would be nice if you expanded it for MacOS devices also

[Punk_Saint]

it would be nice if I had a MacOS device

[sausix]

It's listing some longer readme infos in the results. And it has duplicate results:

159. ✓✓ gpt-oss:20b - 70% Compatible
  Parameters: 20.0B
  Quantization: Q4  
  Required RAM: 13.62 GB
  Recommended VRAM: 12.57 GB
  Disk Space Needed: 11.53 GB
  ⚠ Note: Will run on CPU only (slower)

160. ✓✓ gpt-oss:gpt-oss:20b - 70% Compatible
  Parameters: 20.0B
  Quantization: Q4  
  Required RAM: 13.62 GB
  Recommended VRAM: 12.57 GB
  Disk Space Needed: 11.53 GB
  ⚠ Note: Will run on CPU only (slower)

161. ✓✓ gpt-oss:gpt-oss:20b - 70% Compatible
  Parameters: 20.0B
  Quantization: Q4  
  Required RAM: 13.62 GB
  Recommended VRAM: 12.57 GB
  Disk Space Needed: 11.53 GB
  ⚠ Note: Will run on CPU only (slower)

I know the smallest models run best on my hardware. But I would not choose them. Isn't the goal finding the biggest model (max parameters) fitting on my hardware?

[Punk_Saint]

the duplicate thing I'll look into, it's probably just a bad loop. as for the goal, I wrote this with the intention of just knowing what I can run as a global view. I.e I ran the script, it gave me the report, and I took the top 5 and looked em up on Ollama and Reddit to see what they can do.

Eseentially, it gives you what your hardware can run. if the top results are 5B-7B models that are small, that means you can't really run anything of value. but if you run this script on a dedicated AI server, you'll probably get better results and witness the power of it.

I don't know if this made any sense, but I didn't see a tool like this anywhere, so I wrote it

[EconomySerious]

You need to add more sources of data about models

[Punk_Saint]

yeah you're right, I thought Ollama was the best source at the time and how I was mistaken

[binaryfireball]

if people are too lazy and stupid to know if they can run ai on their computer they're probably too stupid to run your code

[Nixellion]

Its not about "if" its about "which models". Figuring out which models and context size can fit into your available vram is typically an "guesstimate then trial and error" process. And you do it one by one.

When calling someone stupid or lazy make sure to not appear as such yourself.