Can Qwen3-Coder 30B A3B Instruct run on MacBook Pro M4 Max 64GB?

YES — Runs Great

S96Excellent

●Measured on real hardware· m4-max-64gb

Qwen3-Coder 30B A3B Instruct needs ~27.9 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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, 52.0 tok/s, Runs well

27.9 GB required46.1 GB available

61% VRAM used

Fit status

Runs well

Decode

52.0 tok/s

TTFT

3722 ms

Safe context

215K

Memory

27.9 GB / 46.1 GB

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 64GB
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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	52.0 tok/s	2030 ms	215K
Coding	S	Runs well	48.0 tok/s	4048 ms	215K
Agentic Coding	S	Runs well	52.0 tok/s	5414 ms	215K
Reasoning	S	Runs well	52.0 tok/s	4399 ms	215K
RAG	S	Runs well	52.0 tok/s	6767 ms	215K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	S87
Q3_K_S	3	14.9 GB	Low	S88
NVFP4	4

Get started

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

Run

ollama run qwen3-coder

Frequently asked questions

See all results for MacBook Pro M4 Max 64GB See all hardware for Qwen3-Coder 30B A3B Instruct

URL: https://willitrunai.com/can-run/qwen-3-coder-30b-a3b-on-m4-max-64gb

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