Ministral 3 8B needs ~12.3 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 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
Runs well
Decode
28.6 tok/s
TTFT
6760 ms
Safe context
94K
Memory
12.3 GB / 23.0 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 | 28.6 tok/s | 3687 ms | 94K |
| Coding | A | Runs well | 28.6 tok/s | 6760 ms | 94K |
| Agentic Coding | A | Runs well | 28.6 tok/s | 9833 ms | 94K |
| Reasoning | A | Runs well | 28.6 tok/s | 7990 ms | 94K |
| RAG | A | Runs well | 28.6 tok/s | 12292 ms | 94K |
How Ministral 3 8B (8B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A75 |
Q3_K_S | 3 | 3.9 GB | Low | A76 |
NVFP4 | 4 | 4.5 GB | Medium | A76 |
Q4_K_M | 4 | 4.9 GB | Medium | A76 |
Q5_K_M | 5 | 5.8 GB | High | A77 |
Q6_K | 6 | 6.6 GB | High | A77 |
Q8_0 | 8 | 8.6 GB | Very High | A79 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A80 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 15.7 tok/s | |
| Qwen 3.5 27B | 27B | A | 7 tok/s | |
| Qwen 3.6 27B | 27B | S | 8.6 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 16.5 tok/s | |
| Qwen 3.5 9B | 9B | S | 25.5 tok/s |
Yes, MacBook Pro M1 Pro 32GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 28.6 tok/s.
Ministral 3 8B (8B parameters) requires approximately 12.3 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 M1 Pro 32GB, Ministral 3 8B achieves approximately 28.6 tokens per second decode speed with a time-to-first-token of 6760ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on MacBook Pro M1 Pro 32GB receives a A grade with 28.6 tok/s and 94K context.
On MacBook Pro M1 Pro 32GB, Ministral 3 8B can safely use up to 94K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 Pro 32GB 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-3-8b-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: