Can Nemotron Nano 9B v2 run on MacBook Pro M4 Pro 24GB?

YES — Runs Great

A83Great

Estimated — low-sample bucket· few comparable runs

Nemotron Nano 9B v2 needs ~11.4 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~41 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) — 11.4 GB, 40.9 tok/s, Runs well

11.4 GB required17.3 GB available

66% VRAM used

Fit status

Runs well

Decode

40.9 tok/s

TTFT

4734 ms

Safe context

54K

Memory

11.4 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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	40.9 tok/s	2582 ms	54K
Coding	A	Runs well	40.9 tok/s	4734 ms	54K
Agentic Coding	A	Runs well	40.9 tok/s	6885 ms	54K
Reasoning	A	Runs well	40.9 tok/s	5594 ms	54K
RAG	A	Runs well	40.9 tok/s	8607 ms	54K

Inference speed

Nemotron Nano 9B v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Nano 9B v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	119.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	90.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	79.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	79.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	74.0	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	46.5	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	18.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A76
Q3_K_S	3	4.4 GB	Low	A77
NVFP4	4	5.0 GB	Medium	A78
Q4_K_M	4	5.5 GB	Medium	A78
Q5_K_M	5	6.5 GB	High	A79
Q6_K	6	7.4 GB	High	A80
Q8_0Best for your GPU	8	9.6 GB	Very High	A81
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your MacBook Pro M4 Pro 24GB can run

Model	Params	Grade	Decode
Magistral Small 2507	24B	A	17.8 tok/s
Devstral Small 2 24B Instruct	24B	A	17.8 tok/s
Qwen 3 14B	14B	S	23.4 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	23 tok/s
Devstral Small 1.1	24B	A	17.8 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run Nemotron Nano 9B v2?

Yes, MacBook Pro M4 Pro 24GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 40.9 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Nemotron Nano 9B v2 achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4734ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on MacBook Pro M4 Pro 24GB receives a A grade with 40.9 tok/s and 54K context.

What context window can Nemotron Nano 9B v2 use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Nemotron Nano 9B v2 can safely use up to 54K 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 24GB as fast as VRAM for Nemotron Nano 9B v2?

Not always. MacBook Pro M4 Pro 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 MacBook Pro M4 Pro 24GB See all hardware for Nemotron Nano 9B v2

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