MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+2)
B
This setup is broadly balanced for this model.24.1 tok/s decode
~$1,999 MSRP
Ministral 8B needs ~9.7 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~29 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
Tight fit
Decode
30.8 tok/s
TTFT
6278 ms
Safe context
29K
Memory
9.7 GB / 11.5 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 | 28.7 tok/s | 3681 ms | 29K |
| Coding | B | Tight fit | 28.7 tok/s | 6748 ms | 29K |
| Agentic Coding | B | Runs with offload | 26.9 tok/s | 10486 ms | 29K |
| Reasoning | B | Tight fit | 28.7 tok/s | 7975 ms | 29K |
| RAG | B | Runs with offload | 26.9 tok/s | 13108 ms | 29K |
How Ministral 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B59 |
Q3_K_S | 3 | 3.9 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
~$1,999 MSRP
Raises estimated decode speed by about 38%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Pro M2 Pro 16GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 28.7 tok/s.
Ministral 8B (8B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Ministral 8B achieves approximately 28.7 tokens per second decode speed with a time-to-first-token of 6748ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on MacBook Pro M2 Pro 16GB receives a B grade with 28.7 tok/s and 29K context.
On MacBook Pro M2 Pro 16GB, Ministral 8B can safely use up to 29K tokens of context. The model's official context limit is 131K, 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/ministral-8b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| B61 |
Q4_K_M | 4 | 4.9 GB | Medium | B62 |
Q5_K_M | 5 | 5.8 GB | High | B62 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | B62 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Not always. MacBook Pro M2 Pro 16GB 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.