Can WizardLM 13B run on MacBook Pro M3 Max 64GB?

YES — Runs Great

A72Great

Estimated from fit model

WizardLM 13B needs ~27.9 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~30 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) — 27.9 GB, 30.3 tok/s, Runs well

27.9 GB required46.1 GB available

61% VRAM used

Fit status

Runs well

Decode

30.3 tok/s

TTFT

6397 ms

Safe context

Memory

27.9 GB / 46.1 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsWizardLM 13B on MacBook Pro M3 Max 64GB
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: 30.3 tok/s decode · 6.4s TTFT (warm) · 76 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	30.3 tok/s	3489 ms	8K
Coding	A	Runs well	30.3 tok/s	6397 ms	8K
Agentic Coding	A	Tight fit	30.3 tok/s	9305 ms	8K
Reasoning	A	Runs well	30.3 tok/s	7560 ms	8K
RAG	A	Tight fit	30.3 tok/s	11631 ms	8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B63
Q3_K_S	3	6.4 GB	Low	B63
NVFP4	4	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B64
Q8_0	8	13.9 GB	Very High	B65
F16Best for your GPU	16	26.7 GB	Maximum	B69

Get started

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

Run

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

Your hardware

More models your MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	36.3 tok/s
Qwen 3.5 27B	27B	S	15.7 tok/s
Qwen 3.6 27B	27B	S	12 tok/s
Qwen 3.6 35B A3B	35B	S	33.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	37.5 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 64GB run WizardLM 13B?

Yes, MacBook Pro M3 Max 64GB can run WizardLM 13B with a A grade (Runs well). Expected decode speed: 30.3 tok/s.

How much VRAM does WizardLM 13B need?

WizardLM 13B (13B parameters) requires approximately 27.9 GB of memory with Q4_K_M quantization.

What is the best quantization for WizardLM 13B?

The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.

What speed will WizardLM 13B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, WizardLM 13B achieves approximately 30.3 tokens per second decode speed with a time-to-first-token of 6397ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on MacBook Pro M3 Max 64GB receives a A grade with 30.3 tok/s and 8K context.

What context window can WizardLM 13B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, WizardLM 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for WizardLM 13B?

Not always. MacBook Pro M3 Max 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M3 Max 64GB See all hardware for WizardLM 13B

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