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URL: https://willitrunai.com/can-run/qwen-3-coder-30b-a3b-on-m2-24gb

⇱ Qwen3-Coder 30B A3B Instruct on Mac mini M2 24GB? No — Alte…

Can Qwen3-Coder 30B A3B Instruct run on Mac mini M2 24GB?

YES — With Q3_K_S

A80Great
○Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~19.9 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q3_K_S quantization, expect ~9 tok/s.

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

Qwen3-Coder 30B A3B Instruct at Q4_K_M needs 23.6 GB — too much for Mac mini M2 24GB (17.3 GB). Runs at Q3_K_S (19.9 GB) with low quality. 2 quantization levels fit.
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

6.4 tok/s

TTFT

30178 ms

Safe context

4K

Memory

23.6 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3-Coder 30B A3B Instruct on Mac mini M2 24GB
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: 6.4 tok/s decode · 30.2s 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 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.7 tok/s15860 ms4K
CodingFToo heavy6.4 tok/s30178 ms4K
Agentic CodingFToo heavy6.0 tok/s47073 ms4K
ReasoningFToo heavy6.4 tok/s35665 ms4K
RAGFToo heavy6.0 tok/s58841 ms4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
11.9 GB
LowS94
Q3_K_S
3
14.9 GB
LowF0
NVFP4
4
17.1 GB
MediumF0
Q4_K_M
4
18.6 GB
MediumF0
Q5_K_M
5
22.0 GB
HighF0
Q6_K
6
25.0 GB
HighF0
Q8_0
8
32.6 GB
Very HighF0
F16
16
62.5 GB
MaximumF0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Upgrade options

Hardware that runs Qwen3-Coder 30B A3B Instruct well

MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
A
Makes the model fit on the accelerator instead of staying completely out of reach.11.7 tok/s decode

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

Raises estimated decode speed by about 83%.

~$799 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)120 GB/s (+20)
S
Makes the model fit on the accelerator instead of staying completely out of reach.13.1 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)120 GB/s (+20)
A
Makes the model fit on the accelerator instead of staying completely out of reach.11.7 tok/s decode

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

Raises estimated decode speed by about 83%.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+8)1792 GB/s (+1692)
S
Makes the model fit on the accelerator instead of staying completely out of reach.130.7 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.

~$1,999 MSRP

Frequently asked questions

See all results for Mac mini M2 24GBSee all hardware for Qwen3-Coder 30B A3B Instruct