MacBook Air M4 24GBBudget pick
24 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.17.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Ministral 8B needs ~9.7 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~14 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
14.3 tok/s
TTFT
13521 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 | 14.3 tok/s | 7375 ms | 29K |
| Coding | B | Tight fit | 14.3 tok/s | 13521 ms | 29K |
| Agentic Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 13.4 tok/s | 21010 ms | 29K |
| Reasoning | B | Tight fit | 14.3 tok/s | 15979 ms | 29K |
| RAG | B | Runs with offload (needs ~0.2 GB host RAM) | 13.4 tok/s | 26262 ms | 29K |
How Ministral 8B (8B params) fits at each quantization level on MacBook Air M2 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 | 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 |
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GBBiggest leapRaises estimated decode speed by about 571%.
~$1,199 MSRP
Yes, MacBook Air M2 16GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 14.3 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 Air M2 16GB, Ministral 8B achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13521ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on MacBook Air M2 16GB receives a B grade with 14.3 tok/s and 29K context.
On MacBook Air M2 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.
Not always. MacBook Air M2 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-8b-on-m2-air-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: