Can Qwen 3.5 9B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

S94Excellent

Measured on real hardware· m4-pro-24gb

Qwen 3.5 9B needs ~11.2 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 11.2 GB, 37.9 tok/s, Runs well

11.2 GB required17.3 GB available

65% VRAM used

Fit status

Runs well

Decode

37.9 tok/s

TTFT

5112 ms

Safe context

60K

Memory

11.2 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on MacBook Pro M4 Pro 24GB
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: 37.9 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	38.0 tok/s	2758 ms	60K
Coding	S	Runs well	38.0 tok/s	5056 ms	60K
Agentic Coding	S	Runs well	37.9 tok/s	7436 ms	60K
Reasoning	S	Runs well	37.9 tok/s	6042 ms	60K
RAG	S	Runs well	37.9 tok/s	9295 ms	60K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S88
Q3_K_S	3	4.4 GB	Low	S89
NVFP4	4	5.0 GB	Medium	S90
Q4_K_M	4	5.5 GB	Medium	S90
Q5_K_M	5	6.5 GB	High	S91
Q6_K	6	7.4 GB	High	S92
Q8_0Best for your GPU	8	9.6 GB	Very High	S93
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run Qwen 3.5 9B?

Yes, MacBook Pro M4 Pro 24GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 38.0 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 11.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Qwen 3.5 9B achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5056ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on MacBook Pro M4 Pro 24GB receives a S grade with 38.0 tok/s and 60K context.

What context window can Qwen 3.5 9B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Qwen 3.5 9B can safely use up to 60K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for Qwen 3.5 9B?

Not always. MacBook Pro M4 Pro 24GB 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.

See all results for MacBook Pro M4 Pro 24GB See all hardware for Qwen 3.5 9B

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