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Qwen 2.5 Math 72B needs ~53.6 GB but MacBook Pro M4 Max 36GB only has 25.9 GB. Try a smaller quantization or lighter model.
Operating mode
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.
Select quantization to explore
27.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36282 ms
Safe context
4K
Memory
53.6 GB / 25.9 GB
Offload
50%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 53.6 GB, but this setup only exposes 25.9 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.3 tok/s | 19790 ms | 4K |
| Coding | F | Too heavy | 2.6 tok/s | 73153 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 52774 ms | 4K |
| Reasoning | F | Too heavy | 5.3 tok/s | 42879 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 65967 ms | 4K |
How Qwen 2.5 Math 72B (72B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | F0 |
Q3_K_S | 3 | 35.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$3,199 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$40,000 MSRP
| Medium |
| F0 |
Q4_K_M | 4 | 43.9 GB | Medium | F0 |
Q5_K_M | 5 | 51.8 GB | High | F0 |
Q6_K | 6 | 59.0 GB | High | F0 |
Q8_0 | 8 | 77.0 GB | Very High | F0 |
F16 | 16 | 147.6 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
