Is unified memory on Mac mini M4 32GB as fast as VRAM for Qwen 2.5 32B?

Not always. Mac mini M4 32GB 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.

Can Qwen 2.5 32B run on Mac mini M4 32GB?

Q: What should I upgrade first if Qwen 2.5 32B feels slow on Mac mini M4 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

B70Good

Estimated — low-sample bucket· few comparable runs

Qwen 2.5 32B needs ~26.2 GB VRAM. Mac mini M4 32GB has 23.0 GB. With NVFP4 quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Qwen 2.5 32B at Q4_K_M needs 27.8 GB — too much for Mac mini M4 32GB (23.0 GB). Runs at NVFP4 (26.2 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.8 GB, exceeds 23.0 GB available

27.8 GB required23.0 GB available

121% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.5 tok/s

TTFT

29726 ms

Safe context

Memory

27.8 GB / 23.0 GB

Offload

20%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 32B on Mac mini M4 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 6.5 tok/s decode · 29.7s TTFT (warm) · 16 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~2.1 GB host RAM)	7.2 tok/s	14760 ms	4K
Coding	F	Too heavy	6.5 tok/s	29726 ms	4K
Agentic Coding	F	Too heavy	5.6 tok/s	50650 ms	4K
Reasoning	F	Too heavy	6.5 tok/s	35130 ms	4K
RAG	F	Too heavy	5.6 tok/s	63312 ms	4K

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A84
Q3_K_SBest for your GPU	3	15.7 GB	Low	A83

Get started

Copy-paste commands to run Qwen 2.5 32B on your machine.

Run

ollama run qwen2.5

Upgrade options

Hardware that runs Qwen 2.5 32B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

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.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)273 GB/s (+153)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 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.

~$1,599 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Makes the model fit on the accelerator instead of staying completely out of reach.25.7 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.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run Qwen 2.5 32B?

Yes, Mac mini M4 32GB can run Qwen 2.5 32B at NVFP4 quantization (Very compromised (needs ~2.2 GB host RAM)). The recommended Q4_K_M requires 27.8 GB which exceeds available memory, but at NVFP4 it needs only 26.2 GB. Expected decode speed: 8.0 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 27.8 GB at Q4_K_M quantization. On Mac mini M4 32GB, it fits at NVFP4 using 26.2 GB.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization is Q4_K_M, but on Mac mini M4 32GB the best fitting quantization is NVFP4, which uses 26.2 GB.

What speed will Qwen 2.5 32B run at on Mac mini M4 32GB?

On Mac mini M4 32GB, Qwen 2.5 32B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24090ms using NVFP4 quantization.

Can Mac mini M4 32GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on Mac mini M4 32GB receives a F grade with 6.5 tok/s and 4K context.

What context window can Qwen 2.5 32B use on Mac mini M4 32GB?

On Mac mini M4 32GB, Qwen 2.5 32B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Mac mini M4 32GB See all hardware for Qwen 2.5 32B

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