Can Qwen3-Coder-Next run on MacBook Pro M4 Max 96GB?

YES — Tight Fit

S90Excellent

▲Estimated — low-sample bucket· few comparable runs

Qwen3-Coder-Next needs ~61.4 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~30 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 61.4 GB, 30.2 tok/s, Tight fit

61.4 GB required69.1 GB available

89% VRAM used

Fit status

Tight fit

Decode

30.2 tok/s

TTFT

6411 ms

Safe context

100K

Memory

61.4 GB / 69.1 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on MacBook Pro M4 Max 96GB
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: 30.2 tok/s decode · 6.4s TTFT (warm) · 76 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	Tight fit	30.2 tok/s	3497 ms	100K
Coding	S	Tight fit	30.2 tok/s	6411 ms	100K
Agentic Coding	S	Tight fit	30.2 tok/s	9325 ms	100K
Reasoning	S	Tight fit	30.2 tok/s	7577 ms	100K
RAG	S	Tight fit	30.2 tok/s	11657 ms	100K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	S86
Q3_K_S	3	39.2 GB	Low	S88
NVFP4	4	44.8 GB	Medium	S88
Q4_K_MBest for your GPU	4	48.8 GB	Medium	S88
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

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Frequently asked questions

See all results for MacBook Pro M4 Max 96GB See all hardware for Qwen3-Coder-Next

URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-m4-max-96gb

⇱ Qwen3-Coder-Next on MacBook Pro M4 Max 96GB? TIGHT FIT