Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Qwen 3.5 27B?

Not always. MacBook Pro M4 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 27B run on MacBook Pro M4 32GB?

Q: What should I upgrade first if Qwen 3.5 27B feels slow on MacBook Pro M4 32GB?

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

YES — With Offload

S89Excellent

Estimated — low-sample bucket· few comparable runs

Qwen 3.5 27B needs ~24.0 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 24.0 GB, 8.6 tok/s, Runs with offload (needs ~0.7 GB host RAM)

24.0 GB required23.0 GB available

104% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.7 GB host RAM)

Decode

8.6 tok/s

TTFT

22417 ms

Safe context

11K

Memory

24.0 GB / 23.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsQwen 3.5 27B on MacBook Pro M4 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: 8.6 tok/s decode · 22.4s TTFT (warm) · 22 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	Runs with offload	9.3 tok/s	11296 ms	11K
Coding	S	Runs with offload (needs ~0.7 GB host RAM)	8.6 tok/s	22417 ms	11K
Agentic Coding	A	Very compromised (needs ~2.5 GB host RAM)	7.3 tok/s	38704 ms	11K
Reasoning	S	Runs with offload (needs ~0.7 GB host RAM)	8.6 tok/s	26492 ms	11K
RAG	A	Very compromised (needs ~2.5 GB host RAM)	7.3 tok/s

Quantization options

How Qwen 3.5 27B (27B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S93
Q3_K_S	3	13.2 GB	Low	S93
NVFP4	4

Get started

Copy-paste commands to run Qwen 3.5 27B on your machine.

Run

ollama run qwen3.5:27b

Your hardware

More models your MacBook Pro M4 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	11.7 tok/s

Frequently asked questions

Can MacBook Pro M4 32GB run Qwen 3.5 27B?

Yes, MacBook Pro M4 32GB can run Qwen 3.5 27B with a S grade (Runs with offload (needs ~0.7 GB host RAM)). Expected decode speed: 8.6 tok/s.

How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B (27B parameters) requires approximately 24.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 27B?

The recommended quantization for Qwen 3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 27B run at on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Qwen 3.5 27B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22417ms using Q4_K_M quantization.

Can MacBook Pro M4 32GB run Qwen 3.5 27B for coding?

For coding workloads, Qwen 3.5 27B on MacBook Pro M4 32GB receives a S grade with 8.6 tok/s and 11K context.

What context window can Qwen 3.5 27B use on MacBook Pro M4 32GB?

On MacBook Pro M4 32GB, Qwen 3.5 27B can safely use up to 11K 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 27B feels slow on MacBook Pro M4 32GB?

See all results for MacBook Pro M4 32GB See all hardware for Qwen 3.5 27B

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