Can Qwen 3.6 35B A3B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Qwen 3.6 35B A3B needs ~37.6 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: LowStack: 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) — 37.6 GB, 29.5 tok/s, Runs well

37.6 GB required69.1 GB available

54% VRAM used

Fit status

Runs well

Decode

29.5 tok/s

TTFT

6567 ms

Safe context

139K

Memory

37.6 GB / 69.1 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on MacBook Pro M2 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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	29.5 tok/s	3582 ms	139K
Coding	S	Runs well	29.5 tok/s	6567 ms	139K
Agentic Coding	S	Runs well	29.5 tok/s	9551 ms	139K
Reasoning	S	Runs well	29.5 tok/s	7760 ms	139K
RAG	S	Runs well	29.5 tok/s	11939 ms	139K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A84
Q3_K_S	3	17.2 GB	Low	A85
NVFP4	4

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 MacBook Pro M2 Max 96GB See all hardware for Qwen 3.6 35B A3B

URL: https://willitrunai.com/can-run/qwen-3.6-35b-a3b-on-m2-max-96gb

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