Ministral 3 8B needs ~10.6 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~29 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
30.8 tok/s
TTFT
6278 ms
Safe context
23K
Memory
10.6 GB / 11.5 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 30.8 tok/s | 3424 ms | 23K |
| Coding | A | Tight fit | 28.7 tok/s | 6748 ms | 23K |
| Agentic Coding | A | Very compromised (needs ~0.5 GB host RAM) | 24.3 tok/s | 11597 ms | 23K |
| Reasoning | A | Tight fit | 30.8 tok/s | 7419 ms | 23K |
| RAG | A | Very compromised (needs ~0.5 GB host RAM) | 24.3 tok/s | 14497 ms |
How Ministral 3 8B (8B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A80 |
Q3_K_S | 3 | 3.9 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 3 8B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
--hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 27.4 tok/s |
Yes, MacBook Pro M2 Pro 16GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 28.7 tok/s.
Ministral 3 8B (8B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, Ministral 3 8B achieves approximately 28.7 tokens per second decode speed with a time-to-first-token of 6748ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on MacBook Pro M2 Pro 16GB receives a A grade with 28.7 tok/s and 23K context.
On MacBook Pro M2 Pro 16GB, Ministral 3 8B can safely use up to 23K 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-3-8b-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 23K |
4.5 GB |
| Medium |
| A82 |
Q4_K_M | 4 | 4.9 GB | Medium | A83 |
Q5_K_M | 5 | 5.8 GB | High | A83 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | A83 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Pro 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.