Can Nemotron Nano 8B run on MacBook Pro M4 16GB?

YES — Tight Fit

A84Great

▲Estimated — low-sample bucket· few comparable runs

Nemotron Nano 8B needs ~9.5 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.5 GB, 18.9 tok/s, Tight fit

9.5 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

18.9 tok/s

TTFT

10237 ms

Safe context

33K

Memory

9.5 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on MacBook Pro M4 16GB
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: 18.9 tok/s decode · 10.2s TTFT (warm) · 47 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	18.9 tok/s	5584 ms	33K
Coding	A	Tight fit	18.9 tok/s	10237 ms	33K
Agentic Coding	A	Runs with offload	18.9 tok/s	14891 ms	33K
Reasoning	A	Tight fit	18.9 tok/s	12099 ms	33K
RAG	A	Runs with offload	18.9 tok/s	18614 ms	33K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A85
Q3_K_S	3	3.9 GB	Low	S86
NVFP4	4	4.5 GB	Medium	S87
Q4_K_M	4	4.9 GB	Medium	S87
Q5_K_M	5	5.8 GB	High	S87
Q6_KBest for your GPU	6	6.6 GB	High	S87
Q8_0	8	8.6 GB	Very High	F0
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 M4 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	15.6 tok/s
👁 Alibaba Qwen 3 14B	14B	A	7.5 tok/s

Frequently asked questions

See all results for MacBook Pro M4 16GB See all hardware for Nemotron Nano 8B

URL: https://willitrunai.com/can-run/nemotron-nano-8b-on-m4-16gb

⇱ Nemotron Nano 8B on MacBook Pro M4 16GB? TIGHT FIT