text model · Sarvam · macOS
S Can I run Sarvam-105B on Apple M5 Max (128GB)?
Yes. Sarvam-105B runs on Apple M5 Max (128GB) at Q4_K_M (~67.5 GB of ~96 GB usable).
Runs at Q4_K_M using ~67.5 GB of ~96 GB usable.
- Q4_K_M needed
- ~67.5 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 sarvamai/sarvam-105b-gguf:Q4_K_M lms get sarvamai/sarvam-105b-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
- 105B (MoE, 10.3B active)
- Q4_K_M size
- 64.2 GB
- Context
- 128k
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Sarvam-105B on other hardware
FAQ
Can Apple M5 Max (128GB) run Sarvam-105B?
Yes. Sarvam-105B runs on Apple M5 Max (128GB) at Q4_K_M (~67.5 GB of ~96 GB usable).
How much memory does Sarvam-105B need?
Apple M5 Max (128GB) has room to spare. At Q4_K_M the weights are ~64.2 GB; with KV cache and runtime overhead, budget ~67.5 GB at a 4k context. It is a Mixture-of-Experts model (105B total / 10.3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Sarvam-105B 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.