Can Qwen3-Coder-Next run on MacBook Pro M4 Max 64GB?

YES — With Q2_K

S91Excellent

▲Estimated — low-sample bucket· few comparable runs

Qwen3-Coder-Next needs ~40.4 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q2_K quantization, expect ~40 tok/s.

Runtime: MLXCapacity: TightBandwidth: MediumStack: OptimizedBottleneck: 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.

Qwen3-Coder-Next at Q4_K_M needs 58.0 GB — too much for MacBook Pro M4 Max 64GB (46.1 GB). Runs at Q2_K (40.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 58.0 GB, exceeds 46.1 GB available

58.0 GB required46.1 GB available

126% VRAM needed

11.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

21.7 tok/s

TTFT

8925 ms

Safe context

Memory

58.0 GB / 46.1 GB

Offload

20%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom6.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder-Next 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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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	F	Too heavy	22.0 tok/s	4793 ms	4K
Coding	F	Too heavy	21.7 tok/s	8925 ms	4K
Agentic Coding	F	Too heavy	21.0 tok/s	13379 ms	4K
Reasoning	F	Too heavy	21.7 tok/s	10548 ms	4K
RAG	F	Too heavy	21.0 tok/s	16723 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	31.2 GB	Low	S88
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