Will It Run AI

Can Qwen 2.5 VL 72B run on MacBook Pro M2 Max 96GB?

YES — Tight Fit

S86Excellent
Estimated from fit model

Qwen 2.5 VL 72B needs ~60.1 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Memory bandwidth
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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) — 60.1 GB, 5.7 tok/s, Tight fit
60.1 GB required69.1 GB available
87% VRAM used

Fit status

Tight fit

Decode

5.7 tok/s

TTFT

33702 ms

Safe context

33K

Memory

60.1 GB / 69.1 GB

Memory breakdown

Weights43.9 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 72B on MacBook Pro M2 Max 96GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 5.7 tok/s decode · 33.7s TTFT (warm) · 14 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit5.7 tok/s18383 ms33K
CodingSTight fit5.7 tok/s33702 ms33K
Agentic CodingSTight fit5.7 tok/s49020 ms33K
ReasoningSTight fit5.7 tok/s39829 ms33K
RAGSTight fit5.7 tok/s61276 ms33K

Quantization options

How Qwen 2.5 VL 72B (72B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowS86
Q3_K_S
3
35.3 GB
LowS88
NVFP4
4
40.3 GB
MediumS88
Q4_K_M
4
43.9 GB
MediumS88
Q5_K_MBest for your GPU
5
51.8 GB
HighS88
Q6_K
6
59.0 GB
HighF0
Q8_0
8
77.0 GB
Very HighF0
F16
16
147.6 GB
MaximumF0

Get started

Copy-paste commands to run Qwen 2.5 VL 72B on your machine.

Run

lms load Qwen2.5-VL-72B-Instruct && lms server start

Your hardware

More models your MacBook Pro M2 Max 96GB can run

ModelParamsGradeDecodeCapabilities
CohereCommand A 111B111BB2.9 tok/s

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Qwen 2.5 VL 72B?

Yes, MacBook Pro M2 Max 96GB can run Qwen 2.5 VL 72B with a S grade (Tight fit). Expected decode speed: 5.7 tok/s.

How much VRAM does Qwen 2.5 VL 72B need?

Qwen 2.5 VL 72B (72B parameters) requires approximately 60.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 VL 72B?

The recommended quantization for Qwen 2.5 VL 72B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 VL 72B run at on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Qwen 2.5 VL 72B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33702ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Qwen 2.5 VL 72B for coding?

For coding workloads, Qwen 2.5 VL 72B on MacBook Pro M2 Max 96GB receives a S grade with 5.7 tok/s and 33K context.

What context window can Qwen 2.5 VL 72B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, Qwen 2.5 VL 72B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 VL 72B feels slow on MacBook Pro M2 Max 96GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on MacBook Pro M2 Max 96GB as fast as VRAM for Qwen 2.5 VL 72B?

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 96GBSee all hardware for Qwen 2.5 VL 72B
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