Is unified memory on MacBook Air M3 24GB as fast as VRAM for Nemotron 3 Nano 30B?

Not always. MacBook Air M3 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.

Can Nemotron 3 Nano 30B run on MacBook Air M3 24GB?

Q: What should I upgrade first if Nemotron 3 Nano 30B feels slow on MacBook Air M3 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

B68Good

Estimated from fit model

Nemotron 3 Nano 30B needs ~20.6 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q3_K_S quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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.

Nemotron 3 Nano 30B at Q4_K_M needs 24.2 GB — too much for MacBook Air M3 24GB (17.3 GB). Runs at Q3_K_S (20.6 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.2 GB, exceeds 17.3 GB available

24.2 GB required17.3 GB available

140% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.5 tok/s

TTFT

76726 ms

Safe context

Memory

24.2 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron 3 Nano 30B on MacBook Air M3 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: 2.5 tok/s decode · 76.7s TTFT (warm) · 6 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 2.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.7 tok/s	39394 ms	4K
Coding	F	Too heavy	2.3 tok/s	82480 ms	4K
Agentic Coding	F	Too heavy	2.3 tok/s	124539 ms	4K
Reasoning	F	Too heavy	2.5 tok/s	90676 ms	4K
RAG	F	Too heavy	2.3 tok/s	155673 ms	4K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	11.7 GB	Low	S91
Q3_K_S	3	14.7 GB	Low	F0

Get started

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

Run

ollama run nemotron-nano:30b

Upgrade options

Hardware that runs Nemotron 3 Nano 30B well

MacBook Pro M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.8.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 240%.

~$799 MSRP

Mac mini M4 64GBBudget pick

64 GB Unified (+40)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.9.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 32GBApple upgrade

32 GB Unified (+8)120 GB/s (+20)

Makes the model fit on the accelerator instead of staying completely out of reach.8.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 240%.

~$1,099 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+16)1555 GB/s (+1455)

Makes the model fit on the accelerator instead of staying completely out of reach.76.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run Nemotron 3 Nano 30B?

Yes, MacBook Air M3 24GB can run Nemotron 3 Nano 30B at Q3_K_S quantization (Very compromised (needs ~2.4 GB host RAM)). The recommended Q4_K_M requires 24.2 GB which exceeds available memory, but at Q3_K_S it needs only 20.6 GB. Expected decode speed: 3.5 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 24.2 GB at Q4_K_M quantization. On MacBook Air M3 24GB, it fits at Q3_K_S using 20.6 GB.

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

The recommended quantization is Q4_K_M, but on MacBook Air M3 24GB the best fitting quantization is Q3_K_S, which uses 20.6 GB.

What speed will Nemotron 3 Nano 30B run at on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Nemotron 3 Nano 30B achieves approximately 3.5 tokens per second decode speed with a time-to-first-token of 54632ms using Q3_K_S quantization.

Can MacBook Air M3 24GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on MacBook Air M3 24GB receives a F grade with 2.3 tok/s and 4K context.

What context window can Nemotron 3 Nano 30B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Nemotron 3 Nano 30B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Air M3 24GB See all hardware for Nemotron 3 Nano 30B

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