text model · Sarvam · macOS
S Can I run Sarvam-M 24B on Apple M5 Max (128GB)?
Yes. Sarvam-M 24B runs on Apple M5 Max (128GB) at Q4_K_M (~16.3 GB of ~96 GB usable).
Runs at Q4_K_M using ~16.3 GB of ~96 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~16.3 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
llama-cli -hf lmstudio-community/sarvam-m-GGUF:Q4_K_M lms get lmstudio-community/sarvam-m-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.
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
- 128 GB unified
- Usable for weights
- ~96 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Sarvam-M 24B on other hardware
FAQ
Can Apple M5 Max (128GB) run Sarvam-M 24B?
Yes. Sarvam-M 24B runs on Apple M5 Max (128GB) at Q4_K_M (~16.3 GB of ~96 GB usable).
How much memory does Sarvam-M 24B need?
Apple M5 Max (128GB) has room to spare. 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.