text model · Sarvam · macOS

Can I run Sarvam-30B on Apple M4 (24GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.

Needs ~21.7 GB Device usable ~16 GB

Needs ~21.7 GB even at Q4_K_M, but only ~16 GB is usable.

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

The gap is about 5.7 GB: Sarvam-30B needs roughly 21.7 GB at Q4_K_M and Apple M4 (24GB) leaves only about 16 GB usable for a model. The lightest tracked hardware that runs Sarvam-30B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-30B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~21.7 GB
Usable on device: ~16 GB
Device memory: 24 GB

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Which quant fits

Quant ladder vs ~16 GB usable

Q2_K

~14.7 GB

Q3_K_M

~16.8 GB

Q4_K_M

~21.7 GB

Q5_K_M

~23.5 GB

Q6_K

~26.7 GB

Q8_0

~34 GB

FP16

~62.1 GB

The line marks Apple M4 (24GB)'s ~16 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: 30B (MoE, 2.4B active)
Q4_K_M size: 19.6 GB
Context: 64k

Full Sarvam-30B requirements →

Device macOS

Memory: 24 GB unified
Usable for weights: ~16 GB
Power draw: ~65 W
Best runtime: Ollama (MLX backend, preview) / MLX direct

Best models for Apple M4 (24GB) →

What you can run instead

Run Sarvam-30B 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 M4 Pro (48GB)

FAQ

Can Apple M4 (24GB) run Sarvam-30B?

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Apple M4 (24GB) only has ~16 GB usable.

How much memory does Sarvam-30B need?

Apple M4 (24GB) does not have enough memory. 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.

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Markdown

[![Sarvam-30B on Apple M4 (24GB)](https://localmodel.run/badge/sarvam-30b/apple-m4-24gb.svg)](https://localmodel.run/can-i-run/sarvam-30b/apple-m4-24gb)

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.