text model · Mistral · macOS

Can I run Devstral Small 2 24B on Apple M4 Pro (24GB)?

Compatibility verdict VRAM threshold engine

Yes, but tight GPU accelerated ~16 tok/s est.

Yes. Devstral Small 2 24B runs on Apple M4 Pro (24GB) at Q4_K_M (~15.4 GB of ~16 GB usable).

Needs ~15.4 GB Device usable ~16 GB

Fits at Q4_K_M (~15.4 GB of ~16 GB usable) but with little headroom. Close other apps, or drop to a 2k context to free about a gigabyte.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Pro (24GB) leaves ~0.6 GB of headroom.

Q4_K_M needed: ~15.4 GB
Usable on device: ~16 GB
Device memory: 24 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~16 GB usable

Q2_K

~12.1 GB

Q3_K_M

~13.7 GB

Q4_K_M

~15.4 GB

Q5_K_M

~19.1 GB

Q6_K

~21.7 GB

Q8_0

~25.3 GB

FP16

~50 GB

The line marks Apple M4 Pro (24GB)'s ~16 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~140 W
Electricity / 1M tokens: ~$0.36
Pays for itself after: ~14,279M tok

At ~$0.15/kWh and the estimated ~16 tok/s, a million generated tokens costs about $0.36 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Apple M4 Pro (24GB) pays for itself after roughly 14,279 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

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run devstral-small-2:24b

llama.cpp

$ llama-cli -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF

Model Mistral

Parameters: 24B
Q4_K_M size: 13.35 GB
Q8_0 size: 23.33 GB
Context: 384k
Ollama tag: devstral-small-2:24b

Full Devstral Small 2 24B requirements →

Device macOS

Memory: 24 GB unified
Usable for weights: ~16 GB
Power draw: ~140 W
Best runtime: Ollama (MLX backend, preview) / MLX direct

Best models for Apple M4 Pro (24GB) →

You could also run

Run Devstral Small 2 24B on other hardware

Apple M4 (24GB)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)

FAQ

Can Apple M4 Pro (24GB) run Devstral Small 2 24B?

Yes. Devstral Small 2 24B runs on Apple M4 Pro (24GB) at Q4_K_M (~15.4 GB of ~16 GB usable).

How much memory does Devstral Small 2 24B need?

It is a tight fit on Apple M4 Pro (24GB). At Q4_K_M the weights are ~13.35 GB; with KV cache and runtime overhead, budget ~15.4 GB at a 4k context.

What is the best tool to run Devstral Small 2 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

[![Devstral Small 2 24B on Apple M4 Pro (24GB)](https://localmodel.run/badge/devstral-small-2-24b/apple-m4-pro-24gb.svg)](https://localmodel.run/can-i-run/devstral-small-2-24b/apple-m4-pro-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.