text model · TinyLlama · macOS
TL Can I run TinyLlama 1.1B on Apple M5 Pro (48GB)?
Yes. TinyLlama 1.1B runs on Apple M5 Pro (48GB) at Q4_K_M (~1.8 GB of ~32 GB usable).
Runs at Q4_K_M using ~1.8 GB of ~32 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.8 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run tinyllama:1.1b llama-cli -hf TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF:Q4_K_M lms get TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
- Parameters
- 1.1B
- Q4_K_M size
- 0.669 GB
- Q8_0 size
- 1.17 GB
- Context
- 2k
- Ollama tag
- tinyllama:1.1b
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run TinyLlama 1.1B on other hardware
FAQ
Can Apple M5 Pro (48GB) run TinyLlama 1.1B?
Yes. TinyLlama 1.1B runs on Apple M5 Pro (48GB) at Q4_K_M (~1.8 GB of ~32 GB usable).
How much memory does TinyLlama 1.1B need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~0.669 GB; with KV cache and runtime overhead, budget ~1.8 GB at a 4k context.
What is the best tool to run TinyLlama 1.1B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Sources
Memory figures are estimates. See methodology.