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URL: https://willitrunai.com/can-run/wizardlm-13b-on-m3-24gb

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Can WizardLM 13B run on MacBook Pro M3 24GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

WizardLM 13B needs ~23.6 GB but MacBook Pro M3 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity
Share:

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) — 23.6 GB, exceeds 17.3 GB available
23.6 GB required17.3 GB available
136% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.6 tok/s

TTFT

34706 ms

Safe context

8K

Memory

23.6 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsWizardLM 13B on MacBook Pro M3 24GB
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: 5.6 tok/s decode · 34.7s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 23.6 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload8.3 tok/s12734 ms8K
CodingFToo heavy5.6 tok/s34706 ms8K
Agentic CodingFToo heavy3.9 tok/s72977 ms8K
ReasoningFToo heavy5.6 tok/s41016 ms8K
RAGFToo heavy3.9 tok/s91221 ms8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB69
Q3_K_S
3
6.4 GB
LowB70
NVFP4
4

Upgrade options

Hardware that runs WizardLM 13B well

MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 54%.

~$799 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.9.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 32GBApple upgrade
32 GB Unified (+8)120 GB/s (+20)
B
Makes the model fit on the accelerator instead of staying completely out of reach.8.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 54%.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+8)1792 GB/s (+1692)
A
Makes the model fit on the accelerator instead of staying completely out of reach.146.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

See all results for MacBook Pro M3 24GBSee all hardware for WizardLM 13B
7.3 GB
Medium
A71
Q4_K_M
4
7.9 GB
MediumA71
Q5_K_M
5
9.4 GB
HighA72
Q6_KBest for your GPU
6
10.7 GB
HighA71
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.