Can WizardMath 7B run on MacBook Pro M2 Pro 16GB?

YES — Runs Great

A74Great

○Estimated from fit model

WizardMath 7B needs ~8.9 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 8.9 GB, 35.2 tok/s, Runs well

8.9 GB required11.5 GB available

77% VRAM used

Fit status

Runs well

Decode

35.2 tok/s

TTFT

5493 ms

Safe context

Memory

8.9 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsWizardMath 7B on MacBook Pro M2 Pro 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 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	35.2 tok/s	2996 ms	4K
Coding	A	Runs well	35.2 tok/s	5493 ms	4K
Agentic Coding	A	Tight fit	35.2 tok/s	7990 ms	4K
Reasoning	A	Runs well	35.2 tok/s	6492 ms	4K
RAG	A	Tight fit	35.2 tok/s	9987 ms	4K

Quantization options

How WizardMath 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A72
Q4_K_M	4	4.3 GB	Medium	A72
Q5_K_M	5	5.0 GB	High	A73
Q6_K	6	5.7 GB	High	A73
Q8_0Best for your GPU	8	7.5 GB	Very High	A72
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run WizardMath 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
 --hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	27.4 tok/s
👁 Alibaba Qwen 3 14B	14B	A	13.8 tok/s
👁 Alibaba Qwen 3 8B	8B	S	30.8 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	S	30.8 tok/s
👁 Mistral Ministral 3 14B	14B	B	13.7 tok/s

Frequently asked questions

See all results for MacBook Pro M2 Pro 16GB See all hardware for WizardMath 7B

URL: https://willitrunai.com/can-run/wizard-math-7b-on-m2-pro-16gb

⇱ WizardMath 7B on MacBook Pro M2 Pro 16GB? YES