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

YES — Runs Great

S96Excellent

Estimated from fit model

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 ~52 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	52.0 tok/s	3722 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

Inference speed

Qwen3-Coder 30B A3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~182 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	181.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	115.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	104.5	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	99.1	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	84.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	70.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	66.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	52.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	52.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	32.7	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	31.8	Fits
RTX 4070 12GB	12 GB	Q4_K_M	11.4	Too big
RTX 3060 12GB	12 GB	Q4_K_M	7.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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	17.1 GB	Medium	S89
Q4_K_M	4	18.6 GB	Medium	S89
Q5_K_M	5	22.0 GB	High	S91
Q6_K	6	25.0 GB	High	S92
Q8_0Best for your GPU	8	32.6 GB	Very High	S91
F16	16	62.5 GB	Maximum	F0

Get started

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

Run

ollama run qwen3-coder

Frequently asked questions

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

Yes, MacBook Pro M4 Max 64GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 52.0 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 27.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Qwen3-Coder 30B A3B Instruct achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.

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

For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Pro M4 Max 64GB receives a S grade with 52.0 tok/s and 215K context.

What context window can Qwen3-Coder 30B A3B Instruct use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Qwen3-Coder 30B A3B Instruct can safely use up to 215K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Qwen3-Coder 30B A3B Instruct?

Not always. MacBook Pro M4 Max 64GB 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.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3-coder-30b-a3b-on-m4-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

52.0 tok/s

2030 ms

215K

Coding

Runs well

52.0 tok/s

3722 ms

215K

Agentic Coding

Runs well

52.0 tok/s

5414 ms

215K

Reasoning

Runs well

52.0 tok/s

4399 ms

215K

RAG

Runs well

52.0 tok/s

6767 ms

215K

Qwen3-Coder 30B A3B Instruct inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

181.6

Fits

RTX 4090 24GB

24 GB

Q4_K_M

115.8

Offloads

RX 7900 XTX 24GB

24 GB

Q4_K_M

104.5

Offloads

RTX 3090 24GB

24 GB

Q4_K_M

99.1

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

84.2

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

70.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

66.5

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

52.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

52.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

36.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

33.3

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

32.7

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

31.8

Fits

RTX 4070 12GB

12 GB

Q4_K_M

11.4

Too big

RTX 3060 12GB

12 GB

Q4_K_M

7.2

Too big

RTX 4060 8GB

8 GB

Q4_K_M

4.5

Too big

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	17.1 GB	Medium	S89
Q4_K_M	4	18.6 GB	Medium	S89
Q5_K_M	5	22.0 GB	High	S91
Q6_K	6	25.0 GB	High	S92
Q8_0Best for your GPU	8	32.6 GB	Very High	S91
F16	16	62.5 GB	Maximum	F0