Can Granite Code 8B run on MacBook Pro M2 Pro 16GB?

YES — Tight Fit

A75Great

Estimated from fit model

Granite Code 8B needs ~9.5 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 30.8 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

30.8 tok/s

TTFT

6278 ms

Safe context

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGranite Code 8B 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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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	A	Runs well	30.8 tok/s	3424 ms	8K
Coding	A	Tight fit	30.8 tok/s	6278 ms	8K
Agentic Coding	A	Runs with offload	30.8 tok/s	9131 ms	8K
Reasoning	A	Tight fit	30.8 tok/s	7419 ms	8K
RAG	A	Runs with offload	30.8 tok/s	11414 ms	8K

Quantization options

How Granite Code 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A75
Q3_K_S	3	3.9 GB	Low	A76
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A78
Q6_KBest for your GPU	6	6.6 GB	High	A77
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 8B on your machine.

Run

ollama run granite-code:8b

Your hardware

More models your MacBook Pro M2 Pro 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	27.4 tok/s
Qwen 3 14B	14B	A	13.8 tok/s
Ministral 3 14B	14B	B	13.7 tok/s
Nemotron Nano 9B v2	9B	A	27.4 tok/s
CodeGeeX 4 9B	9B	A	27.9 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite Code 8B?

Yes, MacBook Pro M2 Pro 16GB can run Granite Code 8B with a A grade (Tight fit). Expected decode speed: 30.8 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on MacBook Pro M2 Pro 16GB receives a A grade with 30.8 tok/s and 8K context.

What context window can Granite Code 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 8B 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 8B?

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 8B

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<iframe src="https://willitrunai.com/embed/granite-code-8b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

30.8 tok/s

3424 ms

Coding

Tight fit

30.8 tok/s

6278 ms

Agentic Coding

Runs with offload

30.8 tok/s

9131 ms

Reasoning

Tight fit

30.8 tok/s

7419 ms

RAG

Runs with offload

30.8 tok/s

11414 ms

Quantization options

How Granite Code 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A75
Q3_K_S	3	3.9 GB	Low	A76
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A78
Q6_KBest for your GPU	6	6.6 GB	High	A77
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

27.4 tok/s

Qwen 3 14B

14B

13.8 tok/s

Ministral 3 14B

14B

13.7 tok/s

Nemotron Nano 9B v2

27.4 tok/s

CodeGeeX 4 9B

27.9 tok/s

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite Code 8B?

Yes, MacBook Pro M2 Pro 16GB can run Granite Code 8B with a A grade (Tight fit). Expected decode speed: 30.8 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 8B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6278ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on MacBook Pro M2 Pro 16GB receives a A grade with 30.8 tok/s and 8K context.

What context window can Granite Code 8B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 8B 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 8B?