Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for Qwen 3.6 35B A3B?

Not always. MacBook Pro M4 Max 48GB 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 3.6 35B A3B run on MacBook Pro M4 Max 48GB?

Q: What should I upgrade first if Qwen 3.6 35B A3B feels slow on MacBook Pro M4 Max 48GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

S94Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~32.4 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: TransformersCapacity: TightBandwidth: MediumStack: 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) — 32.4 GB, 43.7 tok/s, Tight fit

32.4 GB required34.6 GB available

94% VRAM used

Fit status

Tight fit

Decode

43.7 tok/s

TTFT

4429 ms

Safe context

24K

Memory

32.4 GB / 34.6 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on MacBook Pro M4 Max 48GB
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: 43.7 tok/s decode · 4.4s TTFT (warm) · 109 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	43.7 tok/s	2416 ms	24K
Coding	S	Tight fit	43.7 tok/s	4429 ms	24K
Agentic Coding	A	Runs with offload	36.9 tok/s	7633 ms	24K
Reasoning	S	Tight fit	43.7 tok/s	5234 ms	24K
RAG	A	Runs with offload	36.9 tok/s	9542 ms	24K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S90
Q3_K_S	3	17.2 GB	Low	S91
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

Can MacBook Pro M4 Max 48GB run Qwen 3.6 35B A3B?

Yes, MacBook Pro M4 Max 48GB can run Qwen 3.6 35B A3B with a S grade (Tight fit). Expected decode speed: 43.7 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 32.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 35B A3B?

The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 35B A3B run at on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Qwen 3.6 35B A3B achieves approximately 43.7 tokens per second decode speed with a time-to-first-token of 4429ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 48GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on MacBook Pro M4 Max 48GB receives a S grade with 43.7 tok/s and 24K context.

What context window can Qwen 3.6 35B A3B use on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, Qwen 3.6 35B A3B can safely use up to 24K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.6 35B A3B feels slow on MacBook Pro M4 Max 48GB?

See all results for MacBook Pro M4 Max 48GB See all hardware for Qwen 3.6 35B A3B

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<iframe src="https://willitrunai.com/embed/qwen-3.6-35b-a3b-on-m4-max-48gb" 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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