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⇱ Qwen 2.5 Math 72B on MacBook Pro M3 Max 128GB? YES


Can Qwen 2.5 Math 72B run on MacBook Pro M3 Max 128GB?

YES — Runs Great

B61Good
Estimated from fit model

Qwen 2.5 Math 72B needs ~63.5 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 63.5 GB, 5.9 tok/s, Runs well
63.5 GB required92.2 GB available
69% VRAM used

Fit status

Runs well

Decode

5.9 tok/s

TTFT

32578 ms

Safe context

4K

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 Math 72B on MacBook Pro M3 Max 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: 5.9 tok/s decode · 32.6s TTFT (warm) · 15 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
ChatBRuns well5.9 tok/s17770 ms4K
CodingBRuns well5.9 tok/s32578 ms4K
Agentic CodingBRuns well5.9 tok/s47386 ms4K
ReasoningBRuns well5.9 tok/s38502 ms4K
RAGBRuns well5.9 tok/s59233 ms4K

Quantization options

How Qwen 2.5 Math 72B (72B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
28.1 GB
LowB56
Q3_K_S
3
35.3 GB
LowB58
NVFP4
4
40.3 GB
MediumB59
Q4_K_M
4
43.9 GB
MediumB60
Q5_K_M
5
51.8 GB
HighB61
Q6_K
6
59.0 GB
HighB61
Q8_0Best for your GPU
8
77.0 GB
Very HighB61
F16
16
147.6 GB
MaximumF0

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "Qwen/Qwen2.5-Math-72B-Instruct" \ --hf-file "Qwen2.5-Math-72B-Instruct-Q4_K_M.gguf" \ -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen 2.5 Math 72B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick
1792 GB/s (+1392)
B
Raises estimated decode speed by about 532%.37.3 tok/s decode

Raises estimated decode speed by about 532%.

Moves the workload away from shared memory into dedicated accelerator memory.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value
1597 GB/s (+1197)
B
Raises estimated decode speed by about 463%.33.2 tok/s decode

Raises estimated decode speed by about 463%.

Moves the workload away from shared memory into dedicated accelerator memory.

~$9,999 MSRP

Frequently asked questions

See all results for MacBook Pro M3 Max 128GBSee all hardware for Qwen 2.5 Math 72B