Ministral 8B needs ~10.6 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~13 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
14.3 tok/s
TTFT
13521 ms
Safe context
65K
Memory
10.6 GB / 17.3 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 | 14.3 tok/s | 7375 ms | 65K |
| Coding | B | Runs well | 13.3 tok/s | 14535 ms | 65K |
| Agentic Coding | B | Runs well | 14.3 tok/s | 19667 ms | 65K |
| Reasoning | B | Runs well | 14.3 tok/s | 15979 ms | 65K |
| RAG | B | Runs well | 14.3 tok/s | 24583 ms | 65K |
How Ministral 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B56 |
Q3_K_S | 3 | 3.9 GB | Low | B57 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 334%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 239%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, Mac mini M2 24GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 13.3 tok/s.
Ministral 8B (8B parameters) requires approximately 10.6 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 Mac mini M2 24GB, Ministral 8B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14535ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on Mac mini M2 24GB receives a B grade with 13.3 tok/s and 65K context.
On Mac mini M2 24GB, Ministral 8B can safely use up to 65K 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-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 4.9 GB | Medium | B57 |
Q5_K_M | 5 | 5.8 GB | High | B58 |
Q6_K | 6 | 6.6 GB | High | B59 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | B61 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Not always. Mac mini M2 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.