Ministral 3 14B needs ~13.8 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With NVFP4 quantization, expect ~7 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
3.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.4 tok/s
TTFT
35763 ms
Safe context
4K
Memory
14.5 GB / 11.5 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~1.1 GB host RAM) | 6.1 tok/s | 17264 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 35763 ms | 4K |
| Agentic Coding | F | Too heavy | 4.4 tok/s | 64572 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42265 ms | 4K |
| RAG | F | Too heavy | 4.4 tok/s | 80715 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Ministral 3 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~121 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 120.9 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 77.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.1 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 66.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 61.5 | Tight |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.1 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 25.9 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.3 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 16.3 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 4.5 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Ministral 3 14B (14B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | S88 |
Q3_K_S | 3 | 6.9 GB | Low | S87 |
NVFP4 | 4 | 7.8 GB | Medium | S87 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | S87 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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 99Upgrade-Optionen
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,099 MSRP
RTX A4500 20GBGrößter SprungMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $2,000 MSRP
Yes, MacBook Air M2 16GB can run Ministral 3 14B at NVFP4 quantization (Very compromised (needs ~1.3 GB host RAM)). The recommended Q4_K_M requires 14.5 GB which exceeds available memory, but at NVFP4 it needs only 13.8 GB. Expected decode speed: 6.6 tok/s.
Ministral 3 14B (14B parameters) requires approximately 14.5 GB at Q4_K_M quantization. On MacBook Air M2 16GB, it fits at NVFP4 using 13.8 GB.
The recommended quantization is Q4_K_M, but on MacBook Air M2 16GB the best fitting quantization is NVFP4, which uses 13.8 GB.
On MacBook Air M2 16GB, Ministral 3 14B achieves approximately 6.6 tokens per second decode speed with a time-to-first-token of 29191ms using NVFP4 quantization.
For coding workloads, Ministral 3 14B on MacBook Air M2 16GB receives a F grade with 5.4 tok/s and 4K context.
On MacBook Air M2 16GB, Ministral 3 14B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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-3-14b-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: