Can Qwen 3.5 122B A10B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Qwen 3.5 122B A10B needs ~105.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 105.4 GB, 34.7 tok/s, Runs well

105.4 GB required184.3 GB available

57% VRAM used

Fit status

Runs well

Decode

34.7 tok/s

TTFT

5579 ms

Safe context

131K

Memory

105.4 GB / 184.3 GB

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B on Mac Studio M3 Ultra 256GB
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: 34.7 tok/s decode · 5.6s TTFT (warm) · 87 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	34.7 tok/s	3043 ms	131K
Coding	S	Runs well	34.7 tok/s	5579 ms	131K
Agentic Coding	S	Runs well	34.7 tok/s	8114 ms	131K
Reasoning	S	Runs well	34.7 tok/s	6593 ms	131K
RAG	S	Runs well	34.7 tok/s	10143 ms	131K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	A84
Q3_K_S	3	59.8 GB	Low	S85
NVFP4	4	68.3 GB	Medium	S86
Q4_K_M	4	74.4 GB	Medium	S87
Q5_K_M	5	87.8 GB	High	S88
Q6_K	6	100.0 GB	High	S90
Q8_0Best for your GPU	8	130.5 GB	Very High	S90
F16	16	250.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	S	8.1 tok/s

Frequently asked questions

See all results for Mac Studio M3 Ultra 256GB See all hardware for Qwen 3.5 122B A10B

URL: https://willitrunai.com/can-run/qwen-3.5-122b-a10b-on-m3-ultra-256gb

⇱ Qwen 3.5 122B A10B on Mac Studio M3 Ultra 256GB? YES