Tiny Model Hardware Checker

Check if small LLMs can run on your hardware — from smartwatches to servers

Select Model

Parameters
0.8B
Min RAM Required
0.8 GB
Storage
0.5 GB
Context Length
8K
Quick chatSimple Q&AEmbedded devices

Quick RAM Check

8 GB
Available RAM
🚀
Excellent! 🎉
Plenty of headroom

Hardware Compatibility Matrix

Apple Watch Ultra
Watch
⚠️
RAM:1 GB
Storage:64 GB
Minimum requirements met
Samsung Galaxy Watch
Watch
🚀
RAM:2 GB
Storage:32 GB
Plenty of headroom
Fitbit Sense
Watch
🔶
RAM:0.5 GB
Storage:4 GB
Need 0.8GB RAM, have 0.5GB
ESP32-S3
MCU
❌
RAM:0.008 GB
Storage:0.008 GB
RAM & Storage insufficient
Raspberry Pi Pico
MCU
❌
RAM:0.000264 GB
Storage:0.002 GB
RAM & Storage insufficient
Arduino Nano 33 BLE
MCU
❌
RAM:0.000256 GB
Storage:0.001 GB
RAM & Storage insufficient
Raspberry Pi 5 (8GB)
SBC
🚀
RAM:8 GB
Storage:32 GB
Plenty of headroom
Raspberry Pi 4 (4GB)
SBC
🚀
RAM:4 GB
Storage:16 GB
Plenty of headroom
Jetson Nano
SBC
🚀
RAM:4 GB
Storage:16 GB
Plenty of headroom
Orange Pi 5 (16GB)
SBC
🚀
RAM:16 GB
Storage:32 GB
Plenty of headroom
iPhone 15 Pro
Phone
🚀
RAM:8 GB
Storage:256 GB
Plenty of headroom
Samsung S24 Ultra
Phone
🚀
RAM:12 GB
Storage:256 GB
Plenty of headroom
Pixel 8 Pro
Phone
🚀
RAM:12 GB
Storage:128 GB
Plenty of headroom
MacBook Air M3 (16GB)
Laptop
🚀
RAM:16 GB
Storage:512 GB
Plenty of headroom
MacBook Pro M3 Max (128GB)
Laptop
🚀
RAM:128 GB
Storage:1024 GB
Plenty of headroom
Framework Desktop (Ryzen AI Max)
Desktop
🚀
RAM:128 GB
Storage:2048 GB
Plenty of headroom
Home Server (32GB)
Server
🚀
RAM:32 GB
Storage:1000 GB
Plenty of headroom
Workstation (64GB)
Server
🚀
RAM:64 GB
Storage:2000 GB
Plenty of headroom
Cloud GPU (A100 80GB)
Cloud
🚀
RAM:80 GB
Storage:1000 GB
Plenty of headroom

Did You Know?

0.8B
Qwen 3.5 0.8B can run on a smartwatch!
8MB
ESP32-S3 has enough RAM for tiny models
128K
Phi-3 Mini supports 128K context on phones

Tips for Running Tiny Models

📱
Use Quantization
Q4_K_M quantization reduces memory by 75% with minimal quality loss
⚡
Reduce Context
Shorter context = less memory for KV cache
🔧
Use llama.cpp
Optimized for CPU inference on edge devices
🎯
Match Task to Model
Tiny models excel at specific tasks, not general chat

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How to Use Tiny Model Hardware Checker

Determine if you can cut the cloud cord and run AI on the edge.

  1. 1Select a Tiny/Small LLM (<14B parameters)
  2. 2Select your exact local device (e.g., iPhone 15 Pro, M1 MacBook Air)
  3. 3See the estimated tokens-per-second and memory usage
  4. 4Get instructions for the best app to run it locally (LM Studio/Ollama/MLC)

Who Is Tiny Model Hardware Checker For?

For consumers and developers looking to run private AI locally.

Mobile Developers

Design apps with local-first inference

Privacy Advocates

Run smart models without sending data to OpenAI

Frequently Asked Questions

Can phones really run AI?▼
Yes, using integer quantization (4-bit/8-bit), models like Phi-3 and Qwen 1.5B run impressively fast on modern smartphone NPU/GPUs.

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