text model · Sarvam · macOS

Can I run Sarvam-M 24B on Apple M2 (16GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

Needs ~16.3 GB Device usable ~10.5 GB

Needs ~16.3 GB even at Q4_K_M, but only ~10.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 5.8 GB: Sarvam-M 24B needs roughly 16.3 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Sarvam-M 24B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-M 24B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~16.3 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~12.1 GB

Q3_K_M

~13.7 GB

Q4_K_M

~16.3 GB

Q5_K_M

~19.1 GB

Q6_K

~21.7 GB

Q8_0

~27.1 GB

FP16

~49.2 GB

The line marks Apple M2 (16GB)'s ~10.5 GB budget; rungs past it are too large.

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model Sarvam

Parameters: 24B
Q4_K_M size: 14.3 GB
Q8_0 size: 25.1 GB
Context: 32k

Full Sarvam-M 24B requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Power draw: ~50 W
Best runtime: Ollama (llama.cpp Metal backend) / MLX

Best models for Apple M2 (16GB) →

What you can run instead

Run Sarvam-M 24B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)

FAQ

Can Apple M2 (16GB) run Sarvam-M 24B?

No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.

How much memory does Sarvam-M 24B need?

Apple M2 (16GB) does not have enough memory. 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.

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Markdown

[![Sarvam-M 24B on Apple M2 (16GB)](https://localmodel.run/badge/sarvam-m-24b/apple-m2-16gb.svg)](https://localmodel.run/can-i-run/sarvam-m-24b/apple-m2-16gb)

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.