Can Qwen 2.5 Coder 3B run on MacBook Pro M4 Max 96GB?

YES — Runs Great

B70Good

Estimated from fit model

Qwen 2.5 Coder 3B needs ~15.3 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 15.3 GB, 42.0 tok/s, Runs well

15.3 GB required69.1 GB available

22% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

131K

Memory

15.3 GB / 69.1 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on MacBook Pro M4 Max 96GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	B	Runs well	42.0 tok/s	2514 ms	131K
Coding	B	Runs well	42.0 tok/s	4610 ms	131K
Agentic Coding	A	Runs well	42.0 tok/s	6705 ms	131K
Reasoning	B	Runs well	42.0 tok/s	5448 ms	131K
RAG	A	Runs well	42.0 tok/s	8381 ms	131K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B66
Q3_K_S	3	1.5 GB	Low	B66
NVFP4	4	1.7 GB	Medium	B66
Q4_K_M	4	1.8 GB	Medium	B66
Q5_K_M	5	2.2 GB	High	B66
Q6_K	6	2.5 GB	High	B66
Q8_0	8	3.2 GB	Very High	B66
F16Best for your GPU	16	6.1 GB	Maximum	B66

Get started

Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.

Run

ollama run qwen2.5-coder:3b

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Qwen 2.5 Coder 3B?

Yes, MacBook Pro M4 Max 96GB can run Qwen 2.5 Coder 3B with a B grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Qwen 2.5 Coder 3B need?

Qwen 2.5 Coder 3B (3B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 3B?

The recommended quantization for Qwen 2.5 Coder 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 3B run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Qwen 2.5 Coder 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Qwen 2.5 Coder 3B for coding?

For coding workloads, Qwen 2.5 Coder 3B on MacBook Pro M4 Max 96GB receives a B grade with 42.0 tok/s and 131K context.

What context window can Qwen 2.5 Coder 3B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Qwen 2.5 Coder 3B can safely use up to 131K 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 Max 96GB as fast as VRAM for Qwen 2.5 Coder 3B?

Not always. MacBook Pro M4 Max 96GB 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 Max 96GB See all hardware for Qwen 2.5 Coder 3B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-3b-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: