Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Qwen 3.5 35B A3B?

Not always. MacBook Pro M2 Max 32GB 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 35B A3B run on MacBook Pro M2 Max 32GB?

Q: What should I upgrade first if Qwen 3.5 35B A3B feels slow on MacBook Pro M2 Max 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A76Great

Estimated from fit model

Qwen 3.5 35B A3B needs ~27.2 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 27.2 GB, 27.5 tok/s, Very compromised (needs ~3.2 GB host RAM)

27.2 GB required23.0 GB available

118% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~3.2 GB host RAM)

Decode

27.5 tok/s

TTFT

7052 ms

Safe context

Memory

27.2 GB / 23.0 GB

Offload

20%

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on MacBook Pro M2 Max 32GB
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: 27.5 tok/s decode · 7.1s TTFT (warm) · 69 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 3.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~2.7 GB host RAM)	28.4 tok/s	3714 ms	4K
Coding	A	Very compromised (needs ~3.2 GB host RAM)	27.5 tok/s	7052 ms	4K
Agentic Coding	F	Too heavy	25.7 tok/s	10951 ms	4K
Reasoning	A	Very compromised (needs ~3.2 GB host RAM)	27.5 tok/s	8334 ms	4K
RAG	F	Too heavy	25.7 tok/s	13688 ms

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S92
Q3_K_SBest for your GPU	3	17.2 GB	Low	S91

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

Can MacBook Pro M2 Max 32GB run Qwen 3.5 35B A3B?

Yes, MacBook Pro M2 Max 32GB can run Qwen 3.5 35B A3B with a A grade (Very compromised (needs ~3.2 GB host RAM)). Expected decode speed: 27.5 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 27.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Qwen 3.5 35B A3B achieves approximately 27.5 tokens per second decode speed with a time-to-first-token of 7052ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on MacBook Pro M2 Max 32GB receives a A grade with 27.5 tok/s and 4K context.

What context window can Qwen 3.5 35B A3B use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, Qwen 3.5 35B A3B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Max 32GB See all hardware for Qwen 3.5 35B A3B

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<iframe src="https://willitrunai.com/embed/qwen-3.5-35b-a3b-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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