Will It Run AI

Can WizardLM 13B run on Mac mini M4 64GB?

YES — Runs Great

B68Good
Estimated — low-sample bucket· few comparable runs

WizardLM 13B needs ~27.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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, 9.6 tok/s, Runs well
27.9 GB required46.1 GB available
61% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20192 ms

Safe context

8K

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 Mac mini M4 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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well9.6 tok/s11014 ms8K
CodingBRuns well9.6 tok/s20192 ms8K
Agentic CodingBTight fit9.6 tok/s29370 ms8K
ReasoningBRuns well9.6 tok/s23863 ms8K
RAGBTight fit9.6 tok/s36713 ms8K

Quantization options

How WizardLM 13B (13B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB63
Q3_K_S
3
6.4 GB
LowB63
NVFP4
4
7.3 GB
MediumB63
Q4_K_M
4
7.9 GB
MediumB63
Q5_K_M
5
9.4 GB
HighB64
Q6_K
6
10.7 GB
HighB64
Q8_0
8
13.9 GB
Very HighB65
F16Best for your GPU
16
26.7 GB
MaximumB69

Get started

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

Run

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

アップグレードオプション

WizardLM 13Bを快適に動かすハードウェア

MacBook Pro M4 Max 96GBコスパ重視
96 GB Unified (+32)546 GB/s (+426)
B
Raises estimated decode speed by about 298%.38.2 tok/sデコード

Raises estimated decode speed by about 298%.

Adds memory headroom for longer context windows and future model growth.

〜$2,499 MSRP

MacBook Pro M2 Max 96GBコスパ最良
96 GB Unified (+32)400 GB/s (+280)
B
Raises estimated decode speed by about 205%.29.3 tok/sデコード

Raises estimated decode speed by about 205%.

Adds memory headroom for longer context windows and future model growth.

〜$3,199 MSRP

Mac Studio M3 Ultra 96GBAppleアップグレード
96 GB Unified (+32)819 GB/s (+699)
A
Raises estimated decode speed by about 631%.70.2 tok/sデコード

Raises estimated decode speed by about 631%.

Adds memory headroom for longer context windows and future model growth.

〜$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run WizardLM 13B?

Yes, Mac mini M4 64GB can run WizardLM 13B with a B grade (Runs well). Expected decode speed: 9.6 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 Mac mini M4 64GB?

On Mac mini M4 64GB, WizardLM 13B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20192ms using Q4_K_M quantization.

Can Mac mini M4 64GB run WizardLM 13B for coding?

For coding workloads, WizardLM 13B on Mac mini M4 64GB receives a B grade with 9.6 tok/s and 8K context.

What context window can WizardLM 13B use on Mac mini M4 64GB?

On Mac mini M4 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 Mac mini M4 64GB as fast as VRAM for WizardLM 13B?

Not always. Mac mini M4 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 Mac mini M4 64GBSee all hardware for WizardLM 13B
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