Can Qwen 3.6 35B A3B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

S98Excellent

○Estimated from fit model

Qwen 3.6 35B A3B needs ~34.2 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: TransformersCapacity: 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) — 34.2 GB, 59.0 tok/s, Runs well

34.2 GB required46.1 GB available

74% VRAM used

Fit status

Runs well

Decode

59.0 tok/s

TTFT

3283 ms

Safe context

62K

Memory

34.2 GB / 46.1 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on Mac Studio M2 Ultra 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: 59.0 tok/s decode · 3.3s TTFT (warm) · 147 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	59.0 tok/s	1791 ms	62K
Coding	S	Runs well	59.0 tok/s	3283 ms	62K
Agentic Coding	S	Tight fit	59.0 tok/s	4776 ms	62K
Reasoning	S	Runs well	59.0 tok/s	3880 ms	62K
RAG	S	Tight fit	59.0 tok/s	5970 ms	62K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S87
Q3_K_S	3	17.2 GB	Low	S88
NVFP4	4	19.6 GB	Medium	S89
Q4_K_M	4	21.3 GB	Medium	S89
Q5_K_M	5	25.2 GB	High	S91
Q6_K	6	28.7 GB	High	S90
Q8_0Best for your GPU	8	37.5 GB	Very High	S90
F16	16	71.8 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "Qwen/Qwen3.6-35B-A3B" \
 --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for Qwen 3.6 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.6-35b-a3b-on-m2-ultra-64gb

⇱ Qwen 3.6 35B A3B on Mac Studio M2 Ultra 64GB? YES