Ministral 3 14B needs ~15.4 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~10 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
9.6 tok/s
TTFT
20228 ms
Safe context
28K
Memory
15.4 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 | A | Runs well | 9.6 tok/s | 11034 ms | 28K |
| Coding | A | Tight fit | 10.1 tok/s | 19136 ms | 28K |
| Agentic Coding | A | Runs with offload (needs ~0.3 GB host RAM) | 8.4 tok/s | 33667 ms | 28K |
| Reasoning | A | Tight fit | 9.6 tok/s | 23906 ms | 28K |
| RAG | A | Runs with offload (needs ~0.3 GB host RAM) | 8.4 tok/s | 42084 ms |
How Ministral 3 14B (14B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A84 |
Q3_K_S | 3 | 6.9 GB | Low | A85 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 3 14B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \
--hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Phi-4-reasoning-plus 14B | 14.7B | S | 9.4 tok/s | |
 GPT-OSS 20B | 21B | A | 12.7 tok/s |
Yes, MacBook Air M4 24GB can run Ministral 3 14B with a A grade (Tight fit). Expected decode speed: 10.1 tok/s.
Ministral 3 14B (14B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M4 24GB, Ministral 3 14B achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19136ms using Q4_K_M quantization.
For coding workloads, Ministral 3 14B on MacBook Air M4 24GB receives a A grade with 10.1 tok/s and 28K context.
On MacBook Air M4 24GB, Ministral 3 14B can safely use up to 28K tokens of context. The model's official context limit is 262K, 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-3-14b-on-m4-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 28K |
7.8 GB |
| Medium |
| S86 |
Q4_K_M | 4 | 8.5 GB | Medium | S86 |
Q5_K_M | 5 | 10.1 GB | High | S86 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | S86 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Not always. MacBook Air M4 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.