RAM by quantization
Lower quantization = less RAM but lower quality. Q4_K_M is the recommended sweet spot for most users.
| Format | Bits | RAM | Quality | Verdict |
|---|
| Q4_K_MREC | 4 | 0.8 GB | Good | Runs great |
| Q8_0 | 8 | 1.2 GB | Excellent | Runs great |
| F16 | 16 | 2.2 GB | Lossless | Runs great |
Which Mac can run TinyLlama 1.1B?
Based on the recommended Q4_K_M quantization. You need RAM for both the model and your running apps — DevPulse calculates this for you. No CUDA installation. No driver hell. Just Apple Silicon doing what Jensen charges $30K for.
8 GB
Close apps first
~7 GB for apps
16 GB
Runs well
~15 GB for apps
24 GB
Runs great
~23 GB for apps
32 GB
Runs great
~31 GB for apps
36 GB
Runs great
~35 GB for apps
48 GB
Runs great
~47 GB for apps
64 GB
Runs great
~63 GB for apps
96 GB
Runs great
~95 GB for apps
128 GB
Runs great
~127 GB for apps
192 GB
Runs great
~191 GB for apps
Tips for running TinyLlama 1.1B
1 Perfect for testing your local AI setup before committing to larger models
2 Runs comfortably alongside Chrome, VS Code, and Docker on 8 GB Macs
3 Use Q4_K_M quantization — quality is nearly identical to F16 at this size