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URL: https://willitrunai.com/can-run/qwen-3.5-122b-a10b-on-m3-pro-18gb

⇱ Qwen 3.5 122B A10B on MacBook Pro M3 Pro 18GB? No — Alterna…

Can Qwen 3.5 122B A10B run on MacBook Pro M3 Pro 18GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Qwen 3.5 122B A10B needs ~79.7 GB but MacBook Pro M3 Pro 18GB only has 13.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity
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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) — 79.7 GB, exceeds 13.0 GB available
79.7 GB required13.0 GB available
613% VRAM needed

66.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.1 tok/s

TTFT

63051 ms

Safe context

4K

Memory

79.7 GB / 13.0 GB

Offload

80%

Memory breakdown

Weights74.4 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.9 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen 3.5 122B A10B on MacBook Pro M3 Pro 18GB
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: 3.1 tok/s decode · 63.1s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 79.7 GB, but this setup only exposes 13.0 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy3.1 tok/s34392 ms4K
CodingFToo heavy3.1 tok/s63051 ms4K
Agentic CodingFToo heavy3.1 tok/s91711 ms4K
ReasoningFToo heavy3.1 tok/s74515 ms4K
RAGFToo heavy3.1 tok/s114639 ms4K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
47.6 GB
LowF0
Q3_K_S
3
59.8 GB
LowF0
NVFP4
4
68.3 GB
MediumF0
Q4_K_M
4
74.4 GB
MediumF0
Q5_K_M
5
87.8 GB
HighF0
Q6_K
6
100.0 GB
HighF0
Q8_0
8
130.5 GB
Very HighF0
F16
16
250.1 GB
MaximumF0

Upgrade options

Hardware that runs Qwen 3.5 122B A10B well

MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+110)400 GB/s (+250)
S
Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value
128 GB Unified (+110)800 GB/s (+650)
S
Makes the model fit on the accelerator instead of staying completely out of reach.28.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBApple upgrade
128 GB Unified (+110)800 GB/s (+650)
S
Makes the model fit on the accelerator instead of staying completely out of reach.27.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$3,999 MSRP

AMD Instinct MI250X 128GBBiggest leap
128 GB VRAM (+110)3200 GB/s (+3050)
S
Makes the model fit on the accelerator instead of staying completely out of reach.100.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

See all results for MacBook Pro M3 Pro 18GBSee all hardware for Qwen 3.5 122B A10B