Can Nemotron Nano 9B v2 run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

A76Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~36.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~109 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) — 36.5 GB, 109.1 tok/s, Runs well

36.5 GB required184.3 GB available

20% VRAM used

Fit status

Runs well

Decode

109.1 tok/s

TTFT

1775 ms

Safe context

131K

Memory

36.5 GB / 184.3 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Mac Studio M3 Ultra 256GB
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: 109.1 tok/s decode · 1.8s TTFT (warm) · 273 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	A	Runs well	109.1 tok/s	968 ms	131K
Coding	A	Runs well	109.1 tok/s	1775 ms	131K
Agentic Coding	A	Runs well	109.1 tok/s	2582 ms	131K
Reasoning	A	Runs well	109.1 tok/s	2098 ms	131K
RAG	A	Runs well	109.1 tok/s	3228 ms	131K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B67
Q3_K_S	3	4.4 GB	Low	B67
NVFP4	4	5.0 GB	Medium	B67
Q4_K_M	4	5.5 GB	Medium	B67
Q5_K_M	5	6.5 GB	High	B67
Q6_K	6	7.4 GB	High	B67
Q8_0	8	9.6 GB	Very High	B67
F16Best for your GPU	16	18.5 GB	Maximum	B67

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your Mac Studio M3 Ultra 256GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	8.1 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	84.2 tok/s
Qwen 3.5 27B	27B	S	36.5 tok/s
Qwen 3.6 27B	27B	S	27.8 tok/s
Qwen 3.5 122B A10B	122B	S	34.7 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Nemotron Nano 9B v2?

Yes, Mac Studio M3 Ultra 256GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 109.1 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 36.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Nemotron Nano 9B v2 achieves approximately 109.1 tokens per second decode speed with a time-to-first-token of 1775ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Mac Studio M3 Ultra 256GB receives a A grade with 109.1 tok/s and 131K context.

What context window can Nemotron Nano 9B v2 use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Nemotron Nano 9B v2 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Nemotron Nano 9B v2?

Not always. Mac Studio M3 Ultra 256GB 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 M3 Ultra 256GB See all hardware for Nemotron Nano 9B v2

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/nemotron-nano-9b-v2-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: