Can Nemotron Nano 8B run on MacBook Pro M4 Pro 48GB?

Q: Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for Nemotron Nano 8B?

Not always. MacBook Pro M4 Pro 48GB 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.

YES — Runs Great

A82Great

Estimated — low-sample bucket· few comparable runs

Nemotron Nano 8B needs ~12.9 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 12.9 GB, 46.0 tok/s, Runs well

12.9 GB required34.6 GB available

37% VRAM used

Fit status

Runs well

Decode

46.0 tok/s

TTFT

4208 ms

Safe context

131K

Memory

12.9 GB / 34.6 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on MacBook Pro M4 Pro 48GB
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: 46.0 tok/s decode · 4.2s TTFT (warm) · 115 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	46.0 tok/s	2295 ms	131K
Coding	A	Runs well	43.1 tok/s	4494 ms	131K
Agentic Coding	A	Runs well	46.0 tok/s	6120 ms	131K
Reasoning	A	Runs well	46.0 tok/s	4973 ms	131K
RAG	A	Runs well	46.0 tok/s	7650 ms	131K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A78
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4

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 M4 Pro 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	31.8 tok/s
Qwen 3.5 27B	27B	S	22.7 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 48GB run Nemotron Nano 8B?

Yes, MacBook Pro M4 Pro 48GB can run Nemotron Nano 8B with a A grade (Runs well). Expected decode speed: 43.1 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 12.9 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 M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Nemotron Nano 8B achieves approximately 43.1 tokens per second decode speed with a time-to-first-token of 4494ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 48GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on MacBook Pro M4 Pro 48GB receives a A grade with 43.1 tok/s and 131K context.

What context window can Nemotron Nano 8B use on MacBook Pro M4 Pro 48GB?

On MacBook Pro M4 Pro 48GB, Nemotron Nano 8B 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 MacBook Pro M4 Pro 48GB as fast as VRAM for Nemotron Nano 8B?

See all results for MacBook Pro M4 Pro 48GB See all hardware for Nemotron Nano 8B

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