Can Qwen3-Coder-Next run on MacBook Pro M2 Max 96GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Qwen3-Coder-Next needs ~61.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: MLXCapacity: TightBandwidth: LowStack: 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) — 61.4 GB, 22.4 tok/s, Tight fit

61.4 GB required69.1 GB available

89% VRAM used

Fit status

Tight fit

Decode

22.4 tok/s

TTFT

8641 ms

Safe context

100K

Memory

61.4 GB / 69.1 GB

Memory breakdown

Weights48.8 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder-Next on MacBook Pro M2 Max 96GB
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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	Tight fit	22.4 tok/s	4714 ms	100K
Coding	S	Tight fit	22.4 tok/s	8641 ms	100K
Agentic Coding	S	Tight fit	22.4 tok/s	12569 ms	100K
Reasoning	S	Tight fit	22.4 tok/s	10213 ms	100K
RAG	S	Tight fit	22.4 tok/s	15712 ms	100K

Inference speed

Qwen3-Coder-Next inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder-Next at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~49 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	48.9	Fits
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	43.1	Heavy offload
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	40.7	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	39.3	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	38.6	Fits
2× RTX 3090 24GB	48 GB	Q4_K_M	33.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.2	Fits
4× RTX 3060 12GB	48 GB	Q4_K_M	29.6	Heavy offload
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.7	Too big
RTX 5090 32GB	32 GB	Q4_K_M	20.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	10.8	Too big
RTX 4090 24GB	24 GB	Q4_K_M	7.8	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	7.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	6.6	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	6.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-Coder-Next (80B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	31.2 GB	Low	S86
Q3_K_S	3	39.2 GB	Low	S88
NVFP4	4	44.8 GB	Medium	S88
Q4_K_MBest for your GPU	4	48.8 GB	Medium	S88
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

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Qwen3-Coder-Next?

Yes, MacBook Pro M2 Max 96GB can run Qwen3-Coder-Next with a S grade (Tight fit). Expected decode speed: 22.4 tok/s.

How much VRAM does Qwen3-Coder-Next need?

Qwen3-Coder-Next (80B parameters) requires approximately 61.4 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 MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Qwen3-Coder-Next achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8641ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Qwen3-Coder-Next for coding?

For coding workloads, Qwen3-Coder-Next on MacBook Pro M2 Max 96GB receives a S grade with 22.4 tok/s and 100K context.

What context window can Qwen3-Coder-Next use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Qwen3-Coder-Next can safely use up to 100K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Qwen3-Coder-Next?

Not always. MacBook Pro M2 Max 96GB 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 MacBook Pro M2 Max 96GB See all hardware for Qwen3-Coder-Next

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3-coder-next-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: