Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
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VOOZH | about |
MPT-30B-Instruct needs ~50.1 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
24.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.2 tok/s
TTFT
19024 ms
Safe context
4K
Memory
50.1 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 50.1 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 | 7.2 tok/s | 14600 ms | 4K |
| Coding | F | Too heavy | 5.5 tok/s | 35271 ms | 4K |
| Agentic Coding | F | Too heavy | 5.5 tok/s | 51304 ms | 4K |
| Reasoning | F | Too heavy | 10.2 tok/s | 22483 ms | 4K |
| RAG | F | Too heavy | 10.2 tok/s | 34590 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A70 |
Q3_K_S | 3 | 14.7 GB | Low | A70 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 40%.
~$1,599 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
16.8 GB |
| Medium |
| A70 |
Q4_K_MBest for your GPU | 4 | 18.3 GB | Medium | B70 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 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.