Can Qwen 2.5 72B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A81Great

Estimated from fit model

Qwen 2.5 72B needs ~63.5 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 63.5 GB, 11.5 tok/s, Runs well

63.5 GB required92.2 GB available

69% VRAM used

Fit status

Runs well

Decode

11.5 tok/s

TTFT

16851 ms

Safe context

110K

Memory

63.5 GB / 92.2 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 72B on Mac Studio M2 Ultra 128GB
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: 11.5 tok/s decode · 16.9s TTFT (warm) · 29 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	A	Runs well	11.5 tok/s	9191 ms	110K
Coding	A	Runs well	11.5 tok/s	16851 ms	110K
Agentic Coding	A	Runs well	11.5 tok/s	24510 ms	110K
Reasoning	A	Runs well	11.5 tok/s	19915 ms	110K
RAG	A	Runs well	11.5 tok/s	30638 ms	110K

Inference speed

Qwen 2.5 72B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 72B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~17 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	16.7	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	8.8	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	8.1	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	8.1	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.1	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.8	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.6	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	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 2.5 72B (72B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	A74
Q3_K_S	3	35.3 GB	Low	A75
NVFP4	4	40.3 GB	Medium	A77
Q4_K_M	4	43.9 GB	Medium	A77
Q5_K_M	5	51.8 GB	High	A79
Q6_K	6	59.0 GB	High	A79
Q8_0Best for your GPU	8	77.0 GB	Very High	A79
F16	16	147.6 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5:72b

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	6.3 tok/s
Qwen 3.5 122B A10B	122B	S	28.9 tok/s
Mistral Small 4 119B	119B	S	30.8 tok/s
GPT-OSS 120B	117B	S	7.1 tok/s
Command A 111B	111B	S	7.5 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Qwen 2.5 72B?

Yes, Mac Studio M2 Ultra 128GB can run Qwen 2.5 72B with a A grade (Runs well). Expected decode speed: 11.5 tok/s.

How much VRAM does Qwen 2.5 72B need?

Qwen 2.5 72B (72B parameters) requires approximately 63.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 72B?

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

What speed will Qwen 2.5 72B run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen 2.5 72B achieves approximately 11.5 tokens per second decode speed with a time-to-first-token of 16851ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Qwen 2.5 72B for coding?

For coding workloads, Qwen 2.5 72B on Mac Studio M2 Ultra 128GB receives a A grade with 11.5 tok/s and 110K context.

What context window can Qwen 2.5 72B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen 2.5 72B can safely use up to 110K 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 128GB as fast as VRAM for Qwen 2.5 72B?

Not always. Mac Studio M2 Ultra 128GB 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 128GB See all hardware for Qwen 2.5 72B

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