Can Nemotron Nano 8B run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Nemotron Nano 8B needs ~9.7 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~24 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) — 9.7 GB, 24.1 tok/s, Runs well

9.7 GB required13.0 GB available

75% VRAM used

Fit status

Runs well

Decode

24.1 tok/s

TTFT

8026 ms

Safe context

43K

Memory

9.7 GB / 13.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on MacBook Pro M3 Pro 18GB
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: 24.1 tok/s decode · 8.0s TTFT (warm) · 60 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	S	Runs well	24.1 tok/s	4378 ms	43K
Coding	S	Runs well	24.1 tok/s	8026 ms	43K
Agentic Coding	A	Tight fit	24.1 tok/s	11674 ms	43K
Reasoning	S	Runs well	24.1 tok/s	9485 ms	43K
RAG	A	Tight fit	24.1 tok/s	14593 ms	43K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A84
Q3_K_S	3	3.9 GB	Low	A85
NVFP4	4	4.5 GB	Medium	S85
Q4_K_M	4	4.9 GB	Medium	S86
Q5_K_M	5	5.8 GB	High	S87
Q6_K	6	6.6 GB	High	S87
Q8_0Best for your GPU	8	8.6 GB	Very High	S87
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 MacBook Pro M3 Pro 18GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	21.4 tok/s
Qwen 3 14B	14B	A	12.3 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	10.6 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Nemotron Nano 8B?

Yes, MacBook Pro M3 Pro 18GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 24.1 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 9.7 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 MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Nemotron Nano 8B achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8026ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 18GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on MacBook Pro M3 Pro 18GB receives a S grade with 24.1 tok/s and 43K context.

What context window can Nemotron Nano 8B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Nemotron Nano 8B can safely use up to 43K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for Nemotron Nano 8B?

Not always. MacBook Pro M3 Pro 18GB 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 MacBook Pro M3 Pro 18GB See all hardware for Nemotron Nano 8B

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