Can Nemotron 3 Nano 30B run on MacBook Pro M3 Pro 36GB?

YES — With Offload

S86Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~25.5 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 25.5 GB, 6.4 tok/s, Runs with offload

25.5 GB required25.9 GB available

98% VRAM used

Fit status

Runs with offload

Decode

6.4 tok/s

TTFT

30098 ms

Safe context

19K

Memory

25.5 GB / 25.9 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on MacBook Pro M3 Pro 36GB
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: 6.4 tok/s decode · 30.1s TTFT (warm) · 16 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	6.4 tok/s	16417 ms	19K
Coding	S	Runs with offload	6.4 tok/s	30098 ms	19K
Agentic Coding	A	Runs with offload (needs ~1.3 GB host RAM)	5.6 tok/s	49955 ms	19K
Reasoning	S	Runs with offload	6.4 tok/s	35570 ms	19K
RAG	A	Runs with offload (needs ~1.3 GB host RAM)	5.6 tok/s	62444 ms	19K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S90
Q3_K_S	3	14.7 GB	Low	S90
NVFP4	4	16.8 GB	Medium	S90
Q4_K_MBest for your GPU	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your MacBook Pro M3 Pro 36GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	16.6 tok/s
Qwen 3.6 35B A3B	35B	A	12.1 tok/s
Qwen 3.5 35B A3B	35B	A	14.9 tok/s
Qwen 3 32B	32B	A	5.3 tok/s
Qwen 3 30B A3B	30.5B	S	16.6 tok/s

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run Nemotron 3 Nano 30B?

Yes, MacBook Pro M3 Pro 36GB can run Nemotron 3 Nano 30B with a S grade (Runs with offload). Expected decode speed: 6.4 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 25.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Nemotron 3 Nano 30B achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30098ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on MacBook Pro M3 Pro 36GB receives a S grade with 6.4 tok/s and 19K context.

What context window can Nemotron 3 Nano 30B use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Nemotron 3 Nano 30B can safely use up to 19K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron 3 Nano 30B feels slow on MacBook Pro M3 Pro 36GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Nemotron 3 Nano 30B?

Not always. MacBook Pro M3 Pro 36GB 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 36GB See all hardware for Nemotron 3 Nano 30B

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Preview:

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	6.4 tok/s	16417 ms	19K
Coding	S	Runs with offload	6.4 tok/s	30098 ms	19K
Agentic Coding	A	Runs with offload (needs ~1.3 GB host RAM)	5.6 tok/s	49955 ms	19K
Reasoning	S	Runs with offload	6.4 tok/s	35570 ms	19K
RAG	A	Runs with offload (needs ~1.3 GB host RAM)	5.6 tok/s	62444 ms	19K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S90
Q3_K_S	3	14.7 GB	Low	S90
NVFP4	4	16.8 GB	Medium	S90
Q4_K_MBest for your GPU	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

16.6 tok/s

Qwen 3.6 35B A3B

35B

12.1 tok/s

Qwen 3.5 35B A3B

35B

14.9 tok/s

Qwen 3 32B

32B

5.3 tok/s

Qwen 3 30B A3B