Can WizardLM 13B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

B69Good

○Estimated from fit model

WizardLM 13B needs ~34.9 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 34.9 GB, 58.5 tok/s, Runs well

34.9 GB required92.2 GB available

38% VRAM used

Fit status

Runs well

Decode

58.5 tok/s

TTFT

3309 ms

Safe context

Memory

34.9 GB / 92.2 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsWizardLM 13B on Mac Studio M2 Ultra 128GB
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: 58.5 tok/s decode · 3.3s TTFT (warm) · 146 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	B	Runs well	58.5 tok/s	1805 ms	8K
Coding	B	Runs well	58.5 tok/s	3309 ms	8K
Agentic Coding	A	Runs well	58.5 tok/s	4813 ms	8K
Reasoning	B	Runs well	58.5 tok/s	3910 ms	8K
RAG	A	Runs well	58.5 tok/s	6016 ms	8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B60
Q3_K_S	3	6.4 GB	Low	B60
NVFP4	4	7.3 GB	Medium	B60
Q4_K_M	4	7.9 GB	Medium	B60
Q5_K_M	5	9.4 GB	High	B60
Q6_K	6	10.7 GB	High	B60
Q8_0	8	13.9 GB	Very High	B61
F16Best for your GPU	16	26.7 GB	Maximum	B62

Get started

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

Run

lms load WizardLM-13B-V1.0 && lms server start

Upgrade options

Hardware that runs WizardLM 13B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+992)

Raises estimated decode speed by about 211%.182 tok/s decode

Raises estimated decode speed by about 211%.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+797)

Raises estimated decode speed by about 189%.169.2 tok/s decode

Raises estimated decode speed by about 189%.