Can Granite 3.1 8B run on Mac Studio M1 Ultra 64GB?

YES — Runs Great

C53Usable

Estimated from fit model

Granite 3.1 8B needs ~14.6 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 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) — 14.6 GB, 111.5 tok/s, Runs well

14.6 GB required46.1 GB available

32% VRAM used

Fit status

Runs well

Decode

111.5 tok/s

TTFT

1737 ms

Safe context

128K

Memory

14.6 GB / 46.1 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on Mac Studio M1 Ultra 64GB
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: 111.5 tok/s decode · 1.7s TTFT (warm) · 279 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	111.5 tok/s	947 ms	128K
Coding	C	Runs well	111.5 tok/s	1737 ms	128K
Agentic Coding	C	Runs well	111.5 tok/s	2526 ms	128K
Reasoning	C	Runs well	111.5 tok/s	2053 ms	128K
RAG	C	Runs well	111.5 tok/s	3158 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 M1 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C47
F16Best for your GPU	16	16.4 GB	Maximum	C50

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 M1 Ultra 64GB run Granite 3.1 8B?

Yes, Mac Studio M1 Ultra 64GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 111.5 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 14.6 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 M1 Ultra 64GB?

On Mac Studio M1 Ultra 64GB, Granite 3.1 8B achieves approximately 111.5 tokens per second decode speed with a time-to-first-token of 1737ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 64GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Mac Studio M1 Ultra 64GB receives a C grade with 111.5 tok/s and 128K context.

What context window can Granite 3.1 8B use on Mac Studio M1 Ultra 64GB?

On Mac Studio M1 Ultra 64GB, 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 M1 Ultra 64GB as fast as VRAM for Granite 3.1 8B?

Not always. Mac Studio M1 Ultra 64GB 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 M1 Ultra 64GB See all hardware for Granite 3.1 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-m1-ultra-64gb" 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

111.5 tok/s

947 ms

128K

Coding

Runs well

111.5 tok/s

1737 ms

128K

Agentic Coding

Runs well

111.5 tok/s

2526 ms

128K

Reasoning

Runs well

111.5 tok/s

2053 ms

128K

RAG

Runs well

111.5 tok/s

3158 ms

128K

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

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

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C46
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C47
F16Best for your GPU	16	16.4 GB	Maximum	C50

Frequently asked questions

Can Mac Studio M1 Ultra 64GB run Granite 3.1 8B?

Yes, Mac Studio M1 Ultra 64GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 111.5 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 14.6 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 M1 Ultra 64GB?

On Mac Studio M1 Ultra 64GB, Granite 3.1 8B achieves approximately 111.5 tokens per second decode speed with a time-to-first-token of 1737ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 64GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Mac Studio M1 Ultra 64GB receives a C grade with 111.5 tok/s and 128K context.

What context window can Granite 3.1 8B use on Mac Studio M1 Ultra 64GB?

On Mac Studio M1 Ultra 64GB, 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 M1 Ultra 64GB as fast as VRAM for Granite 3.1 8B?