Can Granite Code 3B run on MacBook Pro M2 Pro 16GB?

YES — Runs Great

B68Good

Estimated from fit model

Granite Code 3B needs ~6.9 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 6.9 GB, 42.0 tok/s, Runs well

6.9 GB required11.5 GB available

60% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

Memory

6.9 GB / 11.5 GB

Memory breakdown

Weights1.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGranite Code 3B on MacBook Pro M2 Pro 16GB
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	8K
Coding	B	Runs well	42.0 tok/s	4610 ms	8K
Agentic Coding	A	Runs well	42.0 tok/s	6705 ms	8K
Reasoning	B	Runs well	42.0 tok/s	5448 ms	8K
RAG	A	Runs well	42.0 tok/s	8381 ms	8K

Quantization options

How Granite Code 3B (3B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	B64
Q3_K_S	3	1.5 GB	Low	B64
NVFP4	4	1.7 GB	Medium	B64
Q4_K_M	4	1.8 GB	Medium	B64
Q5_K_M	5	2.2 GB	High	B65
Q6_K	6	2.5 GB	High	B65
Q8_0	8	3.2 GB	Very High	B66
F16Best for your GPU	16	6.1 GB	Maximum	B68

Get started

Copy-paste commands to run Granite Code 3B on your machine.

Run

ollama run granite-code:3b

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite Code 3B?

Yes, MacBook Pro M2 Pro 16GB can run Granite Code 3B with a B grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Granite Code 3B need?

Granite Code 3B (3B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 3B?

The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 3B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 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 M2 Pro 16GB run Granite Code 3B for coding?

For coding workloads, Granite Code 3B on MacBook Pro M2 Pro 16GB receives a B grade with 42.0 tok/s and 8K context.

What context window can Granite Code 3B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 3B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite Code 3B?

Not always. MacBook Pro M2 Pro 16GB 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 M2 Pro 16GB See all hardware for Granite Code 3B

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