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URL: https://willitrunai.com/can-run/qwen-3-coder-next-on-m4-air-24gb

⇱ Qwen3-Coder-Next on MacBook Air M4 24GB? No — Alternatives

Can Qwen3-Coder-Next run on MacBook Air M4 24GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Qwen3-Coder-Next needs ~53.7 GB but MacBook Air M4 24GB only has 17.3 GB. Try a smaller quantization or lighter model.

Runtime: MLXCapacity: No fitBandwidth: Very lowStack: OptimizedBottleneck: Memory capacity
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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) — 53.7 GB, exceeds 17.3 GB available
53.7 GB required17.3 GB available
310% VRAM needed

36.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.8 tok/s

TTFT

51564 ms

Safe context

4K

Memory

53.7 GB / 17.3 GB

Offload

70%

Memory breakdown

Weights48.8 GB
KV Cache1.5 GB
Runtime0.8 GB
Headroom2.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3-Coder-Next 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: 3.8 tok/s decode · 51.6s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 53.7 GB, but this setup only exposes 17.3 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy3.8 tok/s28126 ms4K
CodingFToo heavy3.8 tok/s51564 ms4K
Agentic CodingFToo heavy3.8 tok/s75002 ms4K
ReasoningFToo heavy3.8 tok/s60939 ms4K
RAGFToo heavy3.8 tok/s93753 ms4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
31.2 GB
LowF0
Q3_K_S
3
39.2 GB
LowF0
NVFP4
4
44.8 GB
MediumF0
Q4_K_M
4
48.8 GB
MediumF0
Q5_K_M
5
57.6 GB
HighF0
Q6_K
6
65.6 GB
HighF0
Q8_0
8
85.6 GB
Very HighF0
F16
16
164.0 GB
MaximumF0

Upgrade options

Hardware that runs Qwen3-Coder-Next well

MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+104)400 GB/s (+280)
S
Makes the model fit on the accelerator instead of staying completely out of reach.23.2 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.

~$2,499 MSRP

MacBook Pro M4 Max 96GBBest value
96 GB Unified (+72)546 GB/s (+426)
S
Makes the model fit on the accelerator instead of staying completely out of reach.30.2 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.

~$2,499 MSRP

MacBook Pro M2 Max 96GBApple upgrade
96 GB Unified (+72)400 GB/s (+280)
S
Makes the model fit on the accelerator instead of staying completely out of reach.22.4 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.

~$3,199 MSRP

Frequently asked questions

See all results for MacBook Air M4 24GBSee all hardware for Qwen3-Coder-Next