Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for Qwen 3.5 122B A10B?

Not always. MacBook Pro M4 Max 36GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

Can Qwen 3.5 122B A10B run on MacBook Pro M4 Max 36GB?

Q: What should I upgrade first if Qwen 3.5 122B A10B feels slow on MacBook Pro M4 Max 36GB?

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.

NO — Won't Fit

F0Won't run

Estimated — low-sample bucket· few comparable runs

Qwen 3.5 122B A10B needs ~81.6 GB but MacBook Pro M4 Max 36GB only has 25.9 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 81.6 GB, exceeds 25.9 GB available

81.6 GB required25.9 GB available

315% VRAM needed

55.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.2 tok/s

TTFT

26729 ms

Safe context

Memory

81.6 GB / 25.9 GB

Offload

70%

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.9 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 M4 Max 36GB
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: 7.2 tok/s decode · 26.7s TTFT (warm) · 18 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 81.6 GB, but this setup only exposes 25.9 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.2 tok/s	14579 ms	4K
Coding	F	Too heavy	7.2 tok/s	26729 ms	4K
Agentic Coding	F	Too heavy	7.2 tok/s	38879 ms	4K
Reasoning	F	Too heavy	7.2 tok/s	31589 ms	4K
RAG	F	Too heavy	7.2 tok/s	48598 ms	4K

Inference speed

Qwen 3.5 122B A10B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 122B A10B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~35 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	34.7	Fits

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	F0
Q3_K_S	3	59.8 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen 3.5 122B A10B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+92)

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 (+92)800 GB/s (+390)

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 (+92)800 GB/s (+390)

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 (+92)3200 GB/s (+2790)

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

Can MacBook Pro M4 Max 36GB run Qwen 3.5 122B A10B?

No, Qwen 3.5 122B A10B requires more memory than MacBook Pro M4 Max 36GB provides.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 81.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 122B A10B run at on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Qwen 3.5 122B A10B achieves approximately 7.2 tokens per second decode speed with a time-to-first-token of 26729ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 36GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on MacBook Pro M4 Max 36GB receives a F grade with 7.2 tok/s and 4K context.

What context window can Qwen 3.5 122B A10B use on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 122B A10B feels slow on MacBook Pro M4 Max 36GB?

See all results for MacBook Pro M4 Max 36GB See all hardware for Qwen 3.5 122B A10B

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