Can Qwen3-Coder-Next run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~64.9 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: MLXCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: Balanced

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) — 64.9 GB, 40.7 tok/s, Runs well

64.9 GB required92.2 GB available

70% VRAM used

Fit status

Runs well

Decode

40.7 tok/s

TTFT

4753 ms

Safe context

256K

Memory

64.9 GB / 92.2 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on Mac Studio M2 Ultra 128GB
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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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	S	Runs well	37.5 tok/s	2819 ms	256K
Coding	S	Runs well	37.5 tok/s	5169 ms	256K
Agentic Coding	S	Runs well	37.5 tok/s	7518 ms	256K
Reasoning	S	Runs well	37.5 tok/s	6108 ms	256K
RAG	S	Runs well	37.5 tok/s	9398 ms	256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A83
Q3_K_S	3	39.2 GB	Low	S85
NVFP4	4	44.8 GB	Medium	S87
Q4_K_M	4	48.8 GB	Medium	S87
Q5_K_M	5	57.6 GB	High	S88
Q6_KBest for your GPU	6	65.6 GB	High	S88
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

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode	Capabilities
Command A 111B	111B	S	8.8 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Qwen3-Coder-Next?

Yes, Mac Studio M2 Ultra 128GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 37.5 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 64.9 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Qwen3-Coder-Next is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder-Next run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen3-Coder-Next achieves approximately 37.5 tokens per second decode speed with a time-to-first-token of 5169ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Mac Studio M2 Ultra 128GB receives a S grade with 37.5 tok/s and 256K context.

What context window can Qwen3-Coder-Next use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen3-Coder-Next can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Qwen3-Coder-Next?

Not always. Mac Studio M2 Ultra 128GB 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 Studio M2 Ultra 128GB See all hardware for Qwen3-Coder-Next

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