Intel Arc Pro B60 24GBBest value456 GB/s (+256)
A
Raises estimated decode speed by about 90%.57.7 tok/s decode
Raises estimated decode speed by about 90%.
~$599 MSRP
MPT-7B-Instruct needs ~16.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
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
Fit status
Runs well
Decode
30.4 tok/s
TTFT
6359 ms
Safe context
8K
Memory
16.4 GB / 23.0 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 30.4 tok/s | 3469 ms | 8K |
| Coding | B | Runs well | 30.4 tok/s | 6359 ms | 8K |
| Agentic Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 27.7 tok/s | 10175 ms | 8K |
| Reasoning | B | Runs well | 30.4 tok/s | 7515 ms | 8K |
| RAG | B | Runs with offload (needs ~0.2 GB host RAM) | 27.7 tok/s | 12718 ms |
How MPT-7B-Instruct (7B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B61 |
NVFP4 | 4 |
Copy-paste commands to run MPT-7B-Instruct on your machine.
Run
lms load mpt-7b-instruct && lms server startUpgrade options
Intel Arc Pro B60 24GBBest valueRaises estimated decode speed by about 90%.
~$599 MSRP
Raises estimated decode speed by about 117%.
~$2,499 MSRP
Yes, MacBook Pro M1 Pro 32GB can run MPT-7B-Instruct with a B grade (Runs well). Expected decode speed: 30.4 tok/s.
MPT-7B-Instruct (7B parameters) requires approximately 16.4 GB of memory with Q4_K_M quantization.
The recommended quantization for MPT-7B-Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 32GB, MPT-7B-Instruct achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6359ms using Q4_K_M quantization.
For coding workloads, MPT-7B-Instruct on MacBook Pro M1 Pro 32GB receives a B grade with 30.4 tok/s and 8K context.
On MacBook Pro M1 Pro 32GB, MPT-7B-Instruct can safely use up to 8K 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-7b-instruct-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
3.9 GB |
| Medium |
| B61 |
Q4_K_M | 4 | 4.3 GB | Medium | B61 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B62 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B66 |
Not always. MacBook Pro M1 Pro 32GB 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.