text model · Sarvam · macOS
Can I run Sarvam-1 2B on Apple M4 Pro (24GB)?
Yes. Sarvam-1 2B runs on Apple M4 Pro (24GB) at Q4_K_M (~2.7 GB of ~16 GB usable).
Runs at Q4_K_M using ~2.7 GB of ~16 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (24GB) leaves ~13.3 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~2.7 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~140 W
- Electricity / 1M tokens
- ~$0.04
- Pays for itself after
- ~4,346M tok
At ~$0.15/kWh and the estimated ~141 tok/s, a million generated tokens costs about $0.04 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Apple M4 Pro (24GB) pays for itself after roughly 4,346 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/sarvam-1-GGUF:Q4_K_M lms get bartowski/sarvam-1-GGUF How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 2B
- Q4_K_M size
- 1.55 GB
- Q8_0 size
- 2.69 GB
- Context
- 8k
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~140 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run Sarvam-1 2B on other hardware
FAQ
Can Apple M4 Pro (24GB) run Sarvam-1 2B?
Yes. Sarvam-1 2B runs on Apple M4 Pro (24GB) at Q4_K_M (~2.7 GB of ~16 GB usable).
How much memory does Sarvam-1 2B need?
Apple M4 Pro (24GB) has room to spare. At Q4_K_M the weights are ~1.55 GB; with KV cache and runtime overhead, budget ~2.7 GB at a 4k context.
What is the best tool to run Sarvam-1 2B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/sarvam-1-2b/apple-m4-pro-24gb) Sources
- apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max
- apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence
- developer.apple.com
- github.com/ml-explore
- github.com/raullenchai
- huggingface.co/bartowski
- huggingface.co/sarvamai
- lmstudio.ai
- support.apple.com/en-us/103253
- support.apple.com/en-us/121553
- support.apple.com/en-us/121555
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.