Can Qwen3-Coder-Next run on MacBook Pro M2 Max 32GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen3-Coder-Next needs ~54.5 GB but MacBook Pro M2 Max 32GB only has 23.0 GB. Try a smaller quantization or lighter model.

Runtime: MLXCapacity: No fitBandwidth: LowStack: OptimizedBottleneck: Memory capacity

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) — 54.5 GB, exceeds 23.0 GB available

54.5 GB required23.0 GB available

237% VRAM needed

31.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.1 tok/s

TTFT

19203 ms

Safe context

Memory

54.5 GB / 23.0 GB

Offload

60%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder-Next on MacBook Pro M2 Max 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: 10.1 tok/s decode · 19.2s TTFT (warm) · 25 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 54.5 GB, but this setup only exposes 23.0 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.1 tok/s	10474 ms	4K
Coding	F	Too heavy	10.1 tok/s	19203 ms	4K
Agentic Coding	F	Too heavy	10.1 tok/s	27932 ms	4K
Reasoning	F	Too heavy	10.1 tok/s	22695 ms	4K
RAG	F	Too heavy	10.1 tok/s	34915 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	F0
Q3_K_S	3	39.2 GB	Low	F0
NVFP4	4	44.8 GB	Medium	F0
Q4_K_M	4	48.8 GB	Medium	F0
Q5_K_M	5	57.6 GB	High	F0
Q6_K	6	65.6 GB	High	F0
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Upgrade options

Hardware that runs Qwen3-Coder-Next well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.23.2 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.

~$2,499 MSRP

MacBook Pro M4 Max 96GBBest value

96 GB Unified (+64)546 GB/s (+146)

Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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.

~$2,499 MSRP

MacBook Pro M2 Max 96GBApple upgrade

96 GB Unified (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.22.4 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.

~$3,199 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 32GB run Qwen3-Coder-Next?

No, Qwen3-Coder-Next requires more memory than MacBook Pro M2 Max 32GB provides.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 54.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Qwen3-Coder-Next achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19203ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on MacBook Pro M2 Max 32GB receives a F grade with 10.1 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Qwen3-Coder-Next can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder-Next feels slow on MacBook Pro M2 Max 32GB?

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.

Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Qwen3-Coder-Next?

Not always. MacBook Pro M2 Max 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.

See all results for MacBook Pro M2 Max 32GB See all hardware for Qwen3-Coder-Next

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

10.1 tok/s

10474 ms

Coding

Too heavy

10.1 tok/s

19203 ms

Agentic Coding

Too heavy

10.1 tok/s

27932 ms

Reasoning

Too heavy

10.1 tok/s

22695 ms

RAG

Too heavy

10.1 tok/s

34915 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

31.2 GB

Low

Q3_K_S

39.2 GB

Low

NVFP4

44.8 GB

Medium

Q4_K_M

48.8 GB

Medium

Q5_K_M

57.6 GB

High

Q6_K

65.6 GB

High

Q8_0

85.6 GB

Very High

F16

164.0 GB

Maximum