Can Qwen 3.5 35B A3B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

S97Excellent

Estimated from fit model

Qwen 3.5 35B A3B needs ~30.5 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~83 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) — 30.5 GB, 83.4 tok/s, Runs well

30.5 GB required46.1 GB available

66% VRAM used

Fit status

Runs well

Decode

83.4 tok/s

TTFT

2322 ms

Safe context

131K

Memory

30.5 GB / 46.1 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime0.8 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on Mac Studio M2 Ultra 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: 83.4 tok/s decode · 2.3s TTFT (warm) · 208 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	83.4 tok/s	1267 ms	131K
Coding	S	Runs well	83.4 tok/s	2322 ms	131K
Agentic Coding	S	Runs well	83.4 tok/s	3378 ms	131K
Reasoning	S	Runs well	83.4 tok/s	2745 ms	131K
RAG	S	Runs well	83.4 tok/s	4223 ms	131K

Inference speed

Qwen 3.5 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 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?
RTX 5090 32GB	32 GB	Q4_K_M	139.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	100.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	83.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	79.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	71.1	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	61.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	61.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	60.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	56.9	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	41.5	Tight
RTX 4080 Super 16GB	16 GB	Q4_K_M	25.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	8.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.4	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 Qwen 3.5 35B A3B (35B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S86
Q3_K_S	3	17.2 GB	Low	S87
NVFP4	4	19.6 GB	Medium	S88
Q4_K_M	4	21.3 GB	Medium	S89
Q5_K_M	5	25.2 GB	High	S90
Q6_K	6	28.7 GB	High	S90
Q8_0Best for your GPU	8	37.5 GB	Very High	S89
F16	16	71.8 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Qwen 3.5 35B A3B?

Yes, Mac Studio M2 Ultra 64GB can run Qwen 3.5 35B A3B with a S grade (Runs well). Expected decode speed: 83.4 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 30.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Qwen 3.5 35B A3B achieves approximately 83.4 tokens per second decode speed with a time-to-first-token of 2322ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on Mac Studio M2 Ultra 64GB receives a S grade with 83.4 tok/s and 131K context.

What context window can Qwen 3.5 35B A3B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Qwen 3.5 35B A3B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Qwen 3.5 35B A3B?

Not always. Mac Studio M2 Ultra 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 Studio M2 Ultra 64GB See all hardware for Qwen 3.5 35B A3B

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<iframe src="https://willitrunai.com/embed/qwen-3.5-35b-a3b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

83.4 tok/s

1267 ms

131K

Coding

Runs well

83.4 tok/s

2322 ms

131K

Agentic Coding

Runs well

83.4 tok/s

3378 ms

131K

Reasoning

Runs well

83.4 tok/s

2745 ms

131K

RAG

Runs well

83.4 tok/s

4223 ms

131K

Qwen 3.5 35B A3B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

139.4

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

100.1

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

83.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

79.1

Fits

RTX 4090 24GB

24 GB

Q4_K_M

71.1

Heavy offload

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

61.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

61.8

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

60.0

Heavy offload

RTX 3090 24GB

24 GB

Q4_K_M

56.9

Heavy offload

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.4

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.5

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

41.5

Tight

RTX 4080 Super 16GB

16 GB

Q4_K_M

25.2

Too big

RTX 4070 12GB

12 GB

Q4_K_M

8.8

Too big

RTX 3060 12GB

12 GB

Q4_K_M

5.2

Too big

RTX 4060 8GB

8 GB

Q4_K_M

4.4

Too big

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S86
Q3_K_S	3	17.2 GB	Low	S87
NVFP4	4	19.6 GB	Medium	S88
Q4_K_M	4	21.3 GB	Medium	S89
Q5_K_M	5	25.2 GB	High	S90
Q6_K	6	28.7 GB	High	S90
Q8_0Best for your GPU	8	37.5 GB	Very High	S89
F16	16	71.8 GB	Maximum	F0