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URL: https://willitrunai.com/can-run/aya-expanse-32b-on-m4-16gb

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Can Aya Expanse 32B run on MacBook Pro M4 16GB?

NO — Won't Fit

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

Aya Expanse 32B needs ~24.6 GB but MacBook Pro M4 16GB only has 11.5 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) — 24.6 GB, exceeds 11.5 GB available
24.6 GB required11.5 GB available
214% VRAM needed

13.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49654 ms

Safe context

4K

Memory

24.6 GB / 11.5 GB

Offload

50%

Memory breakdown

Weights19.5 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsAya Expanse 32B on MacBook Pro M4 16GB
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: 3.9 tok/s decode · 49.7s TTFT (warm) · 10 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 24.6 GB, but this setup only exposes 11.5 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
ChatFToo heavy3.9 tok/s27084 ms4K
CodingFToo heavy3.9 tok/s49654 ms4K
Agentic CodingFToo heavy3.9 tok/s72224 ms4K
ReasoningFToo heavy3.9 tok/s58682 ms4K
RAGFToo heavy3.9 tok/s90281 ms4K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowF0
Q3_K_S
3
15.7 GB
LowF0
NVFP4
4

Upgrade options

Hardware that runs Aya Expanse 32B well

MacBook Pro M4 32GBBest value
32 GB Unified (+16)
C
Makes the model fit on the accelerator instead of staying completely out of reach.7 tok/s decode

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

Raises estimated decode speed by about 79%.

~$799 MSRP

Mac mini M4 64GBBudget pick
64 GB Unified (+48)
C
Makes the model fit on the accelerator instead of staying completely out of reach.8.7 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 (+16)
C
Makes the model fit on the accelerator instead of staying completely out of reach.7 tok/s decode

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

Raises estimated decode speed by about 79%.

~$1,099 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+24)1555 GB/s (+1435)
B
Makes the model fit on the accelerator instead of staying completely out of reach.72.8 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.

~$10,000 MSRP

Frequently asked questions

See all results for MacBook Pro M4 16GBSee all hardware for Aya Expanse 32B
17.9 GB
Medium
F0
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 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.