Can Qwen3-Coder-Next run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

S94Excellent

○Estimated from fit model

Qwen3-Coder-Next needs ~64.9 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: MLXCapacity: RoomyBandwidth: HighStack: 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) — 64.9 GB, 40.7 tok/s, Runs well

64.9 GB required92.2 GB available

70% VRAM used

Fit status

Runs well

Decode

40.7 tok/s

TTFT

4753 ms

Safe context

256K

Memory

64.9 GB / 92.2 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on Mac Studio M2 Ultra 128GB
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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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	40.7 tok/s	2592 ms	256K
Coding	S	Runs well	37.5 tok/s	5169 ms	256K
Agentic Coding	S	Runs well	40.7 tok/s	6913 ms	256K
Reasoning	S	Runs well	40.7 tok/s	5617 ms	256K
RAG	S	Runs well	40.7 tok/s	8641 ms	256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A83
Q3_K_S	3	39.2 GB	Low	S85
NVFP4	4

Get started

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

Run

ollama run qwen3-coder-next

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode	Capabilities
👁 Cohere Command A 111B	111B	S	8.8 tok/s

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GB See all hardware for Qwen3-Coder-Next

URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-m2-ultra-128gb

⇱