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URL: https://willitrunai.com/can-run/vicuna-13b-on-m4-pro-24gb

⇱ Vicuna 13B on MacBook Pro M4 Pro 24GB? No — Alternatives

Can Vicuna 13B run on MacBook Pro M4 Pro 24GB?

NO — Won't Fit

F0Won't run
▲Estimated — low-sample bucket· few comparable runs

Vicuna 13B needs ~23.6 GB but MacBook Pro M4 Pro 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity
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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) — 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

15.2 tok/s

TTFT

12758 ms

Safe context

4K

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 feelsVicuna 13B on MacBook Pro M4 Pro 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 15.2 tok/s decode · 12.8s TTFT (warm) · 38 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 offload (needs ~0.1 GB host RAM)22.6 tok/s4681 ms4K
CodingFToo heavy15.2 tok/s12758 ms4K
Agentic CodingFToo heavy10.5 tok/s26826 ms4K
ReasoningFToo heavy15.2 tok/s15077 ms4K
RAGFToo heavy10.5 tok/s33533 ms4K

Quantization options

How Vicuna 13B (13B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowB69
Q3_K_S
3
6.4 GB
LowB70
NVFP4
4
7.3 GB
MediumA71
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

Upgrade options

Hardware that runs Vicuna 13B well

MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)
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.

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

~$799 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)
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)
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.

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

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+8)1792 GB/s (+1519)
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 M4 Pro 24GBSee all hardware for Vicuna 13B