MPT-30B-Instruct needs ~48.8 GB but MacBook Pro M4 Pro 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
8.0 tok/s
TTFT
24078 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 | 8.0 tok/s | 13133 ms | 4K |
| Coding | F | Too heavy | 8.0 tok/s | 24078 ms | 4K |
| Agentic Coding | F | Too heavy | 8.0 tok/s | 35023 ms | 4K |
| Reasoning | F | Too heavy | 8.0 tok/s | 28456 ms | 4K |
| RAG | F | Too heavy | 8.0 tok/s | 43778 ms | 4K |
How MPT-30B-Instruct (30B params) fits at each quantization level on MacBook Pro M4 Pro 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 |
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 79%.
~$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
No, MPT-30B-Instruct requires more memory than MacBook Pro M4 Pro 24GB provides.
MPT-30B-Instruct (30B parameters) requires approximately 48.8 GB of memory with Q5_K_M quantization.
The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 24GB, MPT-30B-Instruct achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24078ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on MacBook Pro M4 Pro 24GB receives a F grade with 8.0 tok/s and 4K context.
On MacBook Pro M4 Pro 24GB, MPT-30B-Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.
Not always. MacBook Pro M4 Pro 24GB 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.
