Can Granite 3.1 8B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C52Usable

Estimated from fit model

Granite 3.1 8B needs ~21.6 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 21.6 GB, 112.0 tok/s, Runs well

21.6 GB required92.2 GB available

23% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

128K

Memory

21.6 GB / 92.2 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on Mac Studio M2 Ultra 128GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	C	Runs well	112.0 tok/s	943 ms	128K
Coding	C	Runs well	112.0 tok/s	1729 ms	128K
Agentic Coding	C	Runs well	112.0 tok/s	2514 ms	128K
Reasoning	C	Runs well	112.0 tok/s	2043 ms	128K
RAG	C	Runs well	112.0 tok/s	3143 ms	128K

Inference speed

Granite 3.1 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 3.1 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	112.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	111.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	95.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	32.9	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C44
Q3_K_S	3	3.9 GB	Low	C44
NVFP4	4	4.5 GB	Medium	C44
Q4_K_M	4	4.9 GB	Medium	C44
Q5_K_M	5	5.8 GB	High	C44
Q6_K	6	6.6 GB	High	C44
Q8_0	8	8.6 GB	Very High	C44
F16Best for your GPU	16	16.4 GB	Maximum	C45

Get started

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

Run

ollama run granite3.1-dense

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Granite 3.1 8B?

Yes, Mac Studio M2 Ultra 128GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 21.6 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Granite 3.1 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Mac Studio M2 Ultra 128GB receives a C grade with 112.0 tok/s and 128K context.

What context window can Granite 3.1 8B use on Mac Studio M2 Ultra 128GB?

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

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Granite 3.1 8B?

Not always. Mac Studio M2 Ultra 128GB 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 Mac Studio M2 Ultra 128GB 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-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: