Ministral 3 3B needs ~6.1 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~22 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
24.0 tok/s
TTFT
8078 ms
Safe context
135K
Memory
6.1 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 | A | Runs well | 22.3 tok/s | 4736 ms | 135K |
| Coding | A | Runs well | 22.3 tok/s | 8684 ms | 135K |
| Agentic Coding | A | Runs well | 22.3 tok/s | 12631 ms | 135K |
| Reasoning | A | Runs well | 22.3 tok/s | 10262 ms | 135K |
| RAG | A | Runs well | 22.3 tok/s | 15788 ms | 135K |
How Ministral 3 3B (3B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B70 |
Q3_K_S | 3 | 1.5 GB | Low | A70 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 3 3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Ministral-3-3B-Instruct-2512" \
--hf-file "Ministral-3-3B-Instruct-2512-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 8 tok/s | |
| Qwen 3.5 4B | 4B | S | 18 tok/s |
Yes, MacBook Air M1 16GB can run Ministral 3 3B with a A grade (Runs well). Expected decode speed: 22.3 tok/s.
Ministral 3 3B (3B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 3B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, Ministral 3 3B achieves approximately 22.3 tokens per second decode speed with a time-to-first-token of 8684ms using Q4_K_M quantization.
For coding workloads, Ministral 3 3B on MacBook Air M1 16GB receives a A grade with 22.3 tok/s and 135K context.
On MacBook Air M1 16GB, Ministral 3 3B can safely use up to 135K 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-3-3b-on-m1-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.7 GB |
| Medium |
| A71 |
Q4_K_M | 4 | 1.8 GB | Medium | A71 |
Q5_K_M | 5 | 2.2 GB | High | A71 |
Q6_K | 6 | 2.5 GB | High | A71 |
Q8_0 | 8 | 3.2 GB | Very High | A72 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | A75 |
| Qwen 3 8B | 8B | S | 9 tok/s |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 18.9 tok/s |
 Nemotron Nano 8B | 8B | A | 9 tok/s |
Not always. MacBook Air M1 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.