text model · Sarvam · macOS
S Can I run Sarvam-30B on Apple M5 Pro (48GB)?
Yes. Sarvam-30B runs on Apple M5 Pro (48GB) at Q4_K_M (~21.7 GB of ~32 GB usable).
Runs at Q4_K_M using ~21.7 GB of ~32 GB usable.
- Q4_K_M needed
- ~21.7 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.
llama-cli -hf sarvamai/sarvam-30b-gguf:Q4_K_M lms get sarvamai/sarvam-30b-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
- 30B (MoE, 2.4B active)
- Q4_K_M size
- 19.6 GB
- Context
- 64k
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Sarvam-30B on other hardware
FAQ
Can Apple M5 Pro (48GB) run Sarvam-30B?
Yes. Sarvam-30B runs on Apple M5 Pro (48GB) at Q4_K_M (~21.7 GB of ~32 GB usable).
How much memory does Sarvam-30B need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~19.6 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Sarvam-30B 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.