Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Granite 4.1 30B?

Not always. MacBook Pro M2 Pro 32GB 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.

Can Granite 4.1 30B run on MacBook Pro M2 Pro 32GB?

Q: What should I upgrade first if Granite 4.1 30B feels slow on MacBook Pro M2 Pro 32GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

B68Good

Estimated from fit model

Granite 4.1 30B needs ~26.6 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 26.6 GB, 6.6 tok/s, Very compromised (needs ~2.4 GB host RAM)

26.6 GB required23.0 GB available

116% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.4 GB host RAM)

Decode

6.6 tok/s

TTFT

29376 ms

Safe context

Memory

26.6 GB / 23.0 GB

Offload

10%

Memory breakdown

Weights18.3 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGranite 4.1 30B on MacBook Pro M2 Pro 32GB
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: 6.6 tok/s decode · 29.4s TTFT (warm) · 17 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload (needs ~1.2 GB host RAM)	7.3 tok/s	14437 ms	4K
Coding	B	Very compromised (needs ~2.4 GB host RAM)	6.6 tok/s	29376 ms	4K
Agentic Coding	F	Too heavy	5.6 tok/s	50602 ms	4K
Reasoning	B	Very compromised (needs ~2.4 GB host RAM)	6.6 tok/s	34717 ms	4K
RAG	F	Too heavy	5.6 tok/s	63252 ms

Quantization options

How Granite 4.1 30B (30B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A83
Q3_K_S	3	14.7 GB	Low	A82
NVFP4Best for your GPU

Get started

Copy-paste commands to run Granite 4.1 30B on your machine.

Run

ollama run granite4.1:30b

Upgrade options

Hardware that runs Granite 4.1 30B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.9.1 tok/s decode

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

Raises estimated decode speed by about 38%.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)273 GB/s (+73)

Removes host-memory offload, which is usually the single biggest latency and throughput win.22.2 tok/s decode

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

Raises estimated decode speed by about 236%.

~$1,599 MSRP

MacBook Pro M3 Pro 36GBApple upgrade

36 GB Unified (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.5.9 tok/s decode

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Granite 4.1 30B?

Yes, MacBook Pro M2 Pro 32GB can run Granite 4.1 30B with a B grade (Very compromised (needs ~2.4 GB host RAM)). Expected decode speed: 6.6 tok/s.

How much VRAM does Granite 4.1 30B need?

Granite 4.1 30B (30B parameters) requires approximately 26.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 30B?

The recommended quantization for Granite 4.1 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 30B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Granite 4.1 30B achieves approximately 6.6 tokens per second decode speed with a time-to-first-token of 29376ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Granite 4.1 30B for coding?

For coding workloads, Granite 4.1 30B on MacBook Pro M2 Pro 32GB receives a B grade with 6.6 tok/s and 4K context.

What context window can Granite 4.1 30B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Granite 4.1 30B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 30B feels slow on MacBook Pro M2 Pro 32GB?

See all results for MacBook Pro M2 Pro 32GB See all hardware for Granite 4.1 30B

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