Can Granite Code 34B run on MacBook Pro M2 Pro 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Granite Code 34B needs ~27.0 GB but MacBook Pro M2 Pro 16GB only has 11.5 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 27.0 GB, exceeds 11.5 GB available

27.0 GB required11.5 GB available

235% VRAM needed

15.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.3 tok/s

TTFT

58832 ms

Safe context

Memory

27.0 GB / 11.5 GB

Offload

60%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 34B 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: 3.3 tok/s decode · 58.8s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 27.0 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.3 tok/s	32090 ms	4K
Coding	F	Too heavy	3.3 tok/s	58832 ms	4K
Agentic Coding	F	Too heavy	3.3 tok/s	85573 ms	4K
Reasoning	F	Too heavy	3.3 tok/s	69528 ms	4K
RAG	F	Too heavy	3.3 tok/s	106967 ms	4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	F0
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Upgrade options

Hardware that runs Granite Code 34B well

Mac mini M4 64GBBudget pick

64 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.8.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+48)273 GB/s (+73)

Makes the model fit on the accelerator instead of staying completely out of reach.19.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple upgrade

36 GB Unified (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.4.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 42%.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Granite Code 34B?

No, Granite Code 34B requires more memory than MacBook Pro M2 Pro 16GB provides.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 27.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 34B achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58832ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on MacBook Pro M2 Pro 16GB receives a F grade with 3.3 tok/s and 4K context.

What context window can Granite Code 34B use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Granite Code 34B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on MacBook Pro M2 Pro 16GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Granite Code 34B?

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 34B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

3.3 tok/s

32090 ms

Coding

Too heavy

3.3 tok/s

58832 ms

Agentic Coding

Too heavy

3.3 tok/s

85573 ms

Reasoning

Too heavy

3.3 tok/s

69528 ms

RAG

Too heavy

3.3 tok/s

106967 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

13.3 GB

Low

Q3_K_S

16.7 GB

Low

NVFP4

19.0 GB

Medium

Q4_K_M

20.7 GB

Medium

Q5_K_M

24.5 GB

High

Q6_K

27.9 GB

High

Q8_0

36.4 GB

Very High

F16

69.7 GB

Maximum