Can Qwen 3.5 122B A10B run on MacBook Pro M4 Max 96GB?

YES — With NVFP4

A75Great

Estimated from fit model

Qwen 3.5 122B A10B needs ~82.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With NVFP4 quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Qwen 3.5 122B A10B at Q4_K_M needs 88.1 GB — too much for MacBook Pro M4 Max 96GB (69.1 GB). Runs at NVFP4 (82.0 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 88.1 GB, exceeds 69.1 GB available

88.1 GB required69.1 GB available

127% VRAM needed

19.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.2 tok/s

TTFT

12778 ms

Safe context

Memory

88.1 GB / 69.1 GB

Offload

20%

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 122B A10B on MacBook Pro M4 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: 15.2 tok/s decode · 12.8s TTFT (warm) · 38 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 10.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	15.4 tok/s	6853 ms	4K
Coding	F	Too heavy	15.2 tok/s	12778 ms	4K
Agentic Coding	F	Too heavy	14.7 tok/s	19204 ms	4K
Reasoning	F	Too heavy	15.2 tok/s	15101 ms	4K
RAG	F	Too heavy	14.7 tok/s	24006 ms	4K

Inference speed

Qwen 3.5 122B A10B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 122B A10B 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 ~35 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	34.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	28.9	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	27.4	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.4	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	11.3	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.6	Too big
RTX 5090 32GB	32 GB	Q4_K_M	7.2	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.5	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	6.4	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.9	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	4.6	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.2	Too big
RTX 3090 24GB	24 GB	Q4_K_M	4.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	3.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	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 Qwen 3.5 122B A10B (122B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	47.6 GB	Low	S90
Q3_K_S	3	59.8 GB	Low	F0
NVFP4	4	68.3 GB	Medium	F0
Q4_K_M	4	74.4 GB	Medium	F0
Q5_K_M	5	87.8 GB	High	F0
Q6_K	6	100.0 GB	High	F0
Q8_0	8	130.5 GB	Very High	F0
F16	16	250.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Opciones de mejora

Hardware que ejecuta bien Qwen 3.5 122B A10B

MacBook Pro M3 Max 128GBOpción económica

128 GB Unified (+32)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.15 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 128GBMejor relación calidad-precio

128 GB Unified (+32)800 GB/s (+254)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28.9 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

Mac Studio M1 Ultra 128GBMejora Apple

128 GB Unified (+32)800 GB/s (+254)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.27.4 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

AMD Instinct MI250X 128GBMayor salto

128 GB VRAM (+32)3200 GB/s (+2654)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.100.3 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.

~$15,000 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Qwen 3.5 122B A10B?

Yes, MacBook Pro M4 Max 96GB can run Qwen 3.5 122B A10B at NVFP4 quantization (Very compromised (needs ~10.8 GB host RAM)). The recommended Q4_K_M requires 88.1 GB which exceeds available memory, but at NVFP4 it needs only 82.0 GB. Expected decode speed: 18.9 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 88.1 GB at Q4_K_M quantization. On MacBook Pro M4 Max 96GB, it fits at NVFP4 using 82.0 GB.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization is Q4_K_M, but on MacBook Pro M4 Max 96GB the best fitting quantization is NVFP4, which uses 82.0 GB.

What speed will Qwen 3.5 122B A10B run at on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Qwen 3.5 122B A10B achieves approximately 18.9 tokens per second decode speed with a time-to-first-token of 10226ms using NVFP4 quantization.

Can MacBook Pro M4 Max 96GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on MacBook Pro M4 Max 96GB receives a F grade with 15.2 tok/s and 4K context.

What context window can Qwen 3.5 122B A10B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 122B A10B feels slow on MacBook Pro M4 Max 96GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Qwen 3.5 122B A10B?

Not always. MacBook Pro M4 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 M4 Max 96GB See all hardware for Qwen 3.5 122B A10B

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