Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 79%.
~$1,099 MSRP
![]() |
VOOZH | about |
MPT-30B-Instruct needs ~48.8 GB but MacBook Air M4 24GB only has 17.3 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
31.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.3 tok/s
TTFT
58581 ms
Safe context
4K
Memory
48.8 GB / 17.3 GB
Offload
60%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 48.8 GB, but this setup only exposes 17.3 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 | 3.3 tok/s | 31954 ms | 4K |
| Coding | F | Too heavy | 3.3 tok/s | 58581 ms | 4K |
| Agentic Coding | F | Too heavy | 3.3 tok/s | 85209 ms | 4K |
| Reasoning | F | Too heavy | 3.3 tok/s | 69233 ms | 4K |
| RAG | F | Too heavy | 3.3 tok/s | 106512 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 11.7 GB | Low | A71 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 4 | 16.8 GB | Medium | F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
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 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 79%.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 333%.
~$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