Is unified memory on MacBook Air M4 24GB as fast as VRAM for Qwen3-Coder 30B A3B Instruct?

Not always. MacBook Air M4 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 Qwen3-Coder 30B A3B Instruct run on MacBook Air M4 24GB?

Q: What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on MacBook Air M4 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

A81Great

Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~19.9 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q3_K_S quantization, expect ~12 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.

Qwen3-Coder 30B A3B Instruct at Q4_K_M needs 23.6 GB — too much for MacBook Air M4 24GB (17.3 GB). Runs at Q3_K_S (19.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.6 GB, exceeds 17.3 GB available

23.6 GB required17.3 GB available

136% VRAM needed

6.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.5 tok/s

TTFT

22703 ms

Safe context

Memory

23.6 GB / 17.3 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder 30B A3B Instruct on MacBook Air M4 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: 8.5 tok/s decode · 22.7s TTFT (warm) · 21 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 10% 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 2.0 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	8.1 tok/s	12975 ms	4K
Coding	F	Too heavy	7.8 tok/s	24690 ms	4K
Agentic Coding	F	Too heavy	7.3 tok/s	38512 ms	4K
Reasoning	F	Too heavy	7.8 tok/s	29179 ms	4K
RAG	F	Too heavy	7.3 tok/s	48140 ms	4K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	11.9 GB	Low	S94
Q3_K_S	3	14.9 GB	Low	F0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Upgrade options

Hardware that runs Qwen3-Coder 30B A3B Instruct well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.11.7 tok/s decode

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

Raises estimated decode speed by about 38%.

~$799 MSRP

Mac mini M4 64GBBest value

64 GB Unified (+40)

Makes the model fit on the accelerator instead of staying completely out of reach.13.1 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)

Makes the model fit on the accelerator instead of staying completely out of reach.11.7 tok/s decode

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

Raises estimated decode speed by about 38%.

~$1,099 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+8)1792 GB/s (+1672)

Makes the model fit on the accelerator instead of staying completely out of reach.130.7 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.

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M4 24GB run Qwen3-Coder 30B A3B Instruct?

Yes, MacBook Air M4 24GB can run Qwen3-Coder 30B A3B Instruct at Q3_K_S quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 23.6 GB which exceeds available memory, but at Q3_K_S it needs only 19.9 GB. Expected decode speed: 12.1 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 23.6 GB at Q4_K_M quantization. On MacBook Air M4 24GB, it fits at Q3_K_S using 19.9 GB.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization is Q4_K_M, but on MacBook Air M4 24GB the best fitting quantization is Q3_K_S, which uses 19.9 GB.

What speed will Qwen3-Coder 30B A3B Instruct run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Qwen3-Coder 30B A3B Instruct achieves approximately 12.1 tokens per second decode speed with a time-to-first-token of 15951ms using Q3_K_S quantization.

Can MacBook Air M4 24GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on MacBook Air M4 24GB receives a F grade with 7.8 tok/s and 4K context.

What context window can Qwen3-Coder 30B A3B Instruct use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Qwen3-Coder 30B A3B Instruct can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for MacBook Air M4 24GB See all hardware for Qwen3-Coder 30B A3B Instruct

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