Can OLMo 2 13B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

A80Great

▲Estimated — low-sample bucket· few comparable runs

OLMo 2 13B needs ~13.9 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.9 GB, 25.2 tok/s, Runs well

13.9 GB required17.3 GB available

80% VRAM used

Fit status

Runs well

Decode

25.2 tok/s

TTFT

7685 ms

Safe context

33K

Memory

13.9 GB / 17.3 GB

Memory breakdown

Weights7.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsOLMo 2 13B on MacBook Pro M4 Pro 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 25.2 tok/s decode · 7.7s TTFT (warm) · 63 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	25.2 tok/s	4192 ms	33K
Coding	A	Runs well	25.2 tok/s	7685 ms	33K
Agentic Coding	A	Tight fit	25.2 tok/s	11178 ms	33K
Reasoning	A	Runs well	25.2 tok/s	9082 ms	33K
RAG	A	Tight fit	25.2 tok/s	13972 ms	33K

Quantization options

How OLMo 2 13B (13B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A75
Q3_K_S	3	6.4 GB	Low	A76
NVFP4	4	7.3 GB	Medium	A77
Q4_K_M	4	7.9 GB	Medium	A78
Q5_K_M	5	9.4 GB	High	A78
Q6_KBest for your GPU	6	10.7 GB	High	A78
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run OLMo 2 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "allenai/OLMo-2-13B-Instruct" \
 --hf-file "OLMo-2-13B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M4 Pro 24GB can run

Model	Params	Grade	Decode
👁 Mistral Magistral Small 2507	24B	A	17.8 tok/s
👁 Mistral Devstral Small 2 24B Instruct	24B	A	17.8 tok/s
👁 Alibaba Qwen 3 14B	14B	S	23.4 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	23 tok/s
👁 Mistral Devstral Small 1.1	24B	A	17.8 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GB See all hardware for OLMo 2 13B

URL: https://willitrunai.com/can-run/olmo-2-13b-on-m4-pro-24gb

⇱ OLMo 2 13B on MacBook Pro M4 Pro 24GB? YES