Ministral 3 3B needs ~6.1 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~42 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
42.0 tok/s
TTFT
4610 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 | 42.0 tok/s | 2514 ms | 135K |
| Coding | A | Runs well | 42.0 tok/s | 4610 ms | 135K |
| Agentic Coding | A | Runs well | 42.0 tok/s | 6705 ms | 135K |
| Reasoning | A | Runs well | 42.0 tok/s | 5448 ms | 135K |
| RAG | A | Runs well | 42.0 tok/s | 8381 ms | 135K |
How Ministral 3 3B (3B params) fits at each quantization level on MacBook Pro M4 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 | 15.6 tok/s | |
| Qwen 3.5 4B | 4B | S | 35 tok/s |
Yes, MacBook Pro M4 16GB can run Ministral 3 3B with a A grade (Runs well). Expected decode speed: 42.0 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 Pro M4 16GB, Ministral 3 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
For coding workloads, Ministral 3 3B on MacBook Pro M4 16GB receives a A grade with 42.0 tok/s and 135K context.
On MacBook Pro M4 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-m4-16gb" 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 | 17.5 tok/s |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 36.9 tok/s |
 Nemotron Nano 8B | 8B | A | 17.5 tok/s |
Not always. MacBook Pro M4 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.