Can Nemotron Nano 8B run on Mac mini M2 24GB?

YES — Runs Great

A84Great

Estimated from fit model

Nemotron Nano 8B needs ~10.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 10.3 GB, 14.3 tok/s, Runs well

10.3 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

14.3 tok/s

TTFT

13521 ms

Safe context

73K

Memory

10.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on Mac mini M2 24GB
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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 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	14.3 tok/s	7375 ms	73K
Coding	A	Runs well	14.3 tok/s	13521 ms	73K
Agentic Coding	S	Runs well	14.3 tok/s	19667 ms	73K
Reasoning	A	Runs well	14.3 tok/s	15979 ms	73K
RAG	S	Runs well	14.3 tok/s	24583 ms	73K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A82
Q3_K_S	3	3.9 GB	Low	A82
NVFP4	4	4.5 GB	Medium	A83
Q4_K_M	4	4.9 GB	Medium	A83
Q5_K_M	5	5.8 GB	High	A84
Q6_K	6	6.6 GB	High	A85
Q8_0Best for your GPU	8	8.6 GB	Very High	S86
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Your hardware

More models your Mac mini M2 24GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	12.7 tok/s
Magistral Small 2507	24B	B	3.7 tok/s
Devstral Small 2 24B Instruct	24B	B	3.7 tok/s
Qwen 3 14B	14B	S	8.2 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	7.8 tok/s

Frequently asked questions

Can Mac mini M2 24GB run Nemotron Nano 8B?

Yes, Mac mini M2 24GB can run Nemotron Nano 8B with a A grade (Runs well). Expected decode speed: 14.3 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

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

What speed will Nemotron Nano 8B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Nemotron Nano 8B achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13521ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on Mac mini M2 24GB receives a A grade with 14.3 tok/s and 73K context.

What context window can Nemotron Nano 8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Nemotron Nano 8B can safely use up to 73K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M2 24GB as fast as VRAM for Nemotron Nano 8B?

Not always. Mac mini M2 24GB 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 mini M2 24GB See all hardware for Nemotron Nano 8B

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