Can Nemotron Nano 8B run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

S87Excellent

▲Estimated — low-sample bucket· few comparable runs

Nemotron Nano 8B needs ~10.3 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~46 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) — 10.3 GB, 46.0 tok/s, Runs well

10.3 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

46.0 tok/s

TTFT

4208 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 MacBook Pro M4 Pro 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: 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	S	Runs well	46.0 tok/s	2295 ms	73K
Coding	S	Runs well	46.0 tok/s	4208 ms	73K
Agentic Coding	S	Runs well	46.0 tok/s	6120 ms	73K
Reasoning	S	Runs well	46.0 tok/s	4973 ms	73K
RAG	S	Runs well	46.0 tok/s	7650 ms	73K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on MacBook Pro M4 Pro 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 MacBook Pro M4 Pro 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	37.9 tok/s
👁 Mistral Magistral Small 2507	24B	A	17.8 tok/s
👁 Mistral Devstral Small 2 24B Instruct	24B	A	17.8 tok/s
👁 Alibaba Qwen 3 14B	14B	S	23.4 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	23 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Pro 24GB See all hardware for Nemotron Nano 8B

URL: https://willitrunai.com/can-run/nemotron-nano-8b-on-m4-pro-24gb

⇱ Nemotron Nano 8B on MacBook Pro M4 Pro 24GB? YES