text model · Sarvam · macOS
Can I run Sarvam-105B on Apple M5 (32GB)?
No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but Apple M5 (32GB) only has ~21 GB usable.
Needs ~67.5 GB even at Q4_K_M, but only ~21 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 46.5 GB: Sarvam-105B needs roughly 67.5 GB at Q4_K_M and Apple M5 (32GB) leaves only about 21 GB usable for a model. The lightest tracked hardware that runs Sarvam-105B is the Apple M4 Max (128GB) at 128 GB. See Sarvam-105B on Apple M4 Max (128GB).
- Q4_K_M needed
- ~67.5 GB
- Usable on device
- ~21 GB
- Device memory
- 32 GB
Which quant fits
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 105B (MoE, 10.3B active)
- Q4_K_M size
- 64.2 GB
- Context
- 128k
- Memory
- 32 GB unified
- Usable for weights
- ~21 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Sarvam-105B on other hardware
FAQ
Can Apple M5 (32GB) run Sarvam-105B?
No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but Apple M5 (32GB) only has ~21 GB usable.
How much memory does Sarvam-105B need?
Apple M5 (32GB) does not have enough memory. 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/sarvam-105b/apple-m5-32gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.