text model · Sarvam · macOS
S Can I run Sarvam-M 24B on Apple M2 (16GB)?
No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
Needs ~16.3 GB even at Q4_K_M, but only ~10.5 GB is usable.
- Q4_K_M needed
- ~16.3 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.). 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
- 24B
- Q4_K_M size
- 14.3 GB
- Q8_0 size
- 25.1 GB
- Context
- 32k
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
Run Sarvam-M 24B on other hardware
FAQ
Can Apple M2 (16GB) run Sarvam-M 24B?
No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
How much memory does Sarvam-M 24B need?
Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~14.3 GB; with KV cache and runtime overhead, budget ~16.3 GB at a 4k context.
What is the best tool to run Sarvam-M 24B 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.