Can Qwen3-Coder-Next run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~78.7 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~49 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) — 78.7 GB, 48.9 tok/s, Runs well

78.7 GB required184.3 GB available

43% VRAM used

Fit status

Runs well

Decode

48.9 tok/s

TTFT

3960 ms

Safe context

256K

Memory

78.7 GB / 184.3 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on Mac Studio M3 Ultra 256GB
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: 48.9 tok/s decode · 4.0s TTFT (warm) · 122 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	48.9 tok/s	2160 ms	256K
Coding	S	Runs well	48.9 tok/s	3960 ms	256K
Agentic Coding	S	Runs well	48.9 tok/s	5760 ms	256K
Reasoning	S	Runs well	48.9 tok/s	4680 ms	256K
RAG	S	Runs well	48.9 tok/s	7200 ms	256K

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	A79
Q3_K_S	3	39.2 GB	Low	A80
NVFP4	4	44.8 GB	Medium	A81
Q4_K_M	4	48.8 GB	Medium	A81
Q5_K_M	5	57.6 GB	High	A82
Q6_K	6	65.6 GB	High	A83
Q8_0Best for your GPU	8	85.6 GB	Very High	S85
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 M3 Ultra 256GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	9.5 tok/s
Qwen 3.5 122B A10B	122B	S	45.1 tok/s
Mistral Small 4 119B	119B	S	48.9 tok/s
GPT-OSS 120B	117B	S	10 tok/s
Command A 111B	111B	S	10.6 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Qwen3-Coder-Next?

Yes, Mac Studio M3 Ultra 256GB can run Qwen3-Coder-Next with a S grade (Runs well). Expected decode speed: 48.9 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 78.7 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 M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Qwen3-Coder-Next achieves approximately 48.9 tokens per second decode speed with a time-to-first-token of 3960ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on Mac Studio M3 Ultra 256GB receives a S grade with 48.9 tok/s and 256K context.

What context window can Qwen3-Coder-Next use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, 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 M3 Ultra 256GB as fast as VRAM for Qwen3-Coder-Next?

Not always. Mac Studio M3 Ultra 256GB 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 M3 Ultra 256GB See all hardware for Qwen3-Coder-Next

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