Can Granite 3.1 8B run on MacBook Air M2 16GB?

Q: Is unified memory on MacBook Air M2 16GB as fast as VRAM for Granite 3.1 8B?

Not always. MacBook Air M2 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.

YES — Tight Fit

C53Usable

Estimated from fit model

Granite 3.1 8B needs ~9.5 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~15 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, 16.5 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

16.5 tok/s

TTFT

11757 ms

Safe context

33K

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 3.1 8B on MacBook Air M2 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: 16.5 tok/s decode · 11.8s TTFT (warm) · 41 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	16.5 tok/s	6413 ms	33K
Coding	C	Tight fit	15.3 tok/s	12639 ms	33K
Agentic Coding	C	Runs with offload	16.5 tok/s	17101 ms	33K
Reasoning	C	Tight fit	16.5 tok/s	13895 ms	33K
RAG	C	Runs with offload	16.5 tok/s	21377 ms	33K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	B56
NVFP4	4

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Upgrade options

Hardware that runs Granite 3.1 8B well

MacBook Air M4 24GBBudget pick

24 GB Unified (+8)120 GB/s (+20)

Raises estimated decode speed by about 33%.21.9 tok/s decode

Raises estimated decode speed by about 33%.

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.17.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Air M3 24GBApple upgrade

24 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.17.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

RTX 3080 Ti 12GBBiggest leap

912 GB/s (+812)

Raises estimated decode speed by about 482%.96 tok/s decode

Raises estimated decode speed by about 482%.

~$1,199 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run Granite 3.1 8B?

Yes, MacBook Air M2 16GB can run Granite 3.1 8B with a C grade (Tight fit). Expected decode speed: 15.3 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Granite 3.1 8B achieves approximately 15.3 tokens per second decode speed with a time-to-first-token of 12639ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on MacBook Air M2 16GB receives a C grade with 15.3 tok/s and 33K context.

What context window can Granite 3.1 8B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Granite 3.1 8B can safely use up to 33K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for Granite 3.1 8B?

See all results for MacBook Air M2 16GB See all hardware for Granite 3.1 8B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: