Ministral 3 14B needs ~16.2 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~28 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
27.7 tok/s
TTFT
6991 ms
Safe context
61K
Memory
16.2 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 | S | Runs well | 27.7 tok/s | 3813 ms | 61K |
| Coding | S | Runs well | 27.7 tok/s | 6991 ms | 61K |
| Agentic Coding | S | Runs well | 27.7 tok/s | 10169 ms | 61K |
| Reasoning | S | Runs well | 27.7 tok/s | 8262 ms | 61K |
| RAG | S | Runs well | 27.7 tok/s | 12711 ms | 61K |
How Ministral 3 14B (14B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A81 |
Q3_K_S | 3 | 6.9 GB | Low | A82 |
NVFP4 | 4 |
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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 26.6 tok/s | |
| Qwen 3.5 27B | 27B | A | 11.8 tok/s |
Yes, MacBook Pro M1 Max 32GB can run Ministral 3 14B with a S grade (Runs well). Expected decode speed: 27.7 tok/s.
Ministral 3 14B (14B parameters) requires approximately 16.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, Ministral 3 14B achieves approximately 27.7 tokens per second decode speed with a time-to-first-token of 6991ms using Q4_K_M quantization.
For coding workloads, Ministral 3 14B on MacBook Pro M1 Max 32GB receives a S grade with 27.7 tok/s and 61K context.
On MacBook Pro M1 Max 32GB, Ministral 3 14B can safely use up to 61K 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-3-14b-on-m1-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.8 GB |
| Medium |
| A83 |
Q4_K_M | 4 | 8.5 GB | Medium | A83 |
Q5_K_M | 5 | 10.1 GB | High | A84 |
Q6_K | 6 | 11.5 GB | High | S85 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | A85 |
F16 | 16 | 28.7 GB | Maximum | F0 |
| Qwen 3.6 27B | 27B | S | 14.5 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | A | 28 tok/s |
 Magistral Small 2507 | 24B | S | 16.2 tok/s |
Not always. MacBook Pro M1 Max 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.