Can Qwen3-Coder-Next run on Mac mini M4 64GB?

YES — With Q2_K

S87Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~40.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: MLXCapacity: TightBandwidth: Very lowStack: OptimizedBottleneck: 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.

Qwen3-Coder-Next at Q4_K_M needs 58.0 GB — too much for Mac mini M4 64GB (46.1 GB). Runs at Q2_K (40.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 58.0 GB, exceeds 46.1 GB available

58.0 GB required46.1 GB available

126% VRAM needed

11.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32303 ms

Safe context

Memory

58.0 GB / 46.1 GB

Offload

20%

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom6.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3-Coder-Next on Mac mini M4 64GB
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: 6.0 tok/s decode · 32.3s TTFT (warm) · 15 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	F	Too heavy	6.1 tok/s	17348 ms	4K
Coding	F	Too heavy	6.0 tok/s	32303 ms	4K
Agentic Coding	F	Too heavy	5.8 tok/s	48422 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	38176 ms	4K
RAG	F	Too heavy	5.8 tok/s	60528 ms	4K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	31.2 GB	Low	S88
Q3_K_S	3	39.2 GB	Low	F0
NVFP4	4	44.8 GB	Medium	F0
Q4_K_M	4	48.8 GB	Medium	F0
Q5_K_M	5	57.6 GB	High	F0
Q6_K	6	65.6 GB	High	F0
Q8_0	8	85.6 GB	Very High	F0
F16	16	164.0 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-Coder-Next on your machine.

Run

ollama run qwen3-coder-next

Opciones de mejora

Hardware que ejecuta bien Qwen3-Coder-Next

MacBook Pro M3 Max 128GBOpción económica

128 GB Unified (+64)400 GB/s (+280)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.23.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$2,499 MSRP

MacBook Pro M4 Max 96GBMejor relación calidad-precio

96 GB Unified (+32)546 GB/s (+426)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.30.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$2,499 MSRP

Mac Studio M2 Ultra 128GBMejora Apple

128 GB Unified (+64)800 GB/s (+680)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.40.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Qwen3-Coder-Next?

Yes, Mac mini M4 64GB can run Qwen3-Coder-Next at Q2_K quantization (Tight fit). The recommended Q4_K_M requires 58.0 GB which exceeds available memory, but at Q2_K it needs only 40.4 GB. Expected decode speed: 11.1 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 58.0 GB at Q4_K_M quantization. On Mac mini M4 64GB, it fits at Q2_K using 40.4 GB.

What is the best quantization for Qwen3-Coder-Next?

The recommended quantization is Q4_K_M, but on Mac mini M4 64GB the best fitting quantization is Q2_K, which uses 40.4 GB.

What speed will Qwen3-Coder-Next run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Qwen3-Coder-Next achieves approximately 11.1 tokens per second decode speed with a time-to-first-token of 17453ms using Q2_K quantization.

Can Mac mini M4 64GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Mac mini M4 64GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Qwen3-Coder-Next use on Mac mini M4 64GB?

On Mac mini M4 64GB, Qwen3-Coder-Next can safely use up to 78K tokens of context at Q2_K quantization. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Qwen3-Coder-Next?

Not always. Mac mini M4 64GB 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 Mac mini M4 64GB See all hardware for Qwen3-Coder-Next

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<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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