Ministral 3 14B needs ~26.6 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~54 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
58.4 tok/s
TTFT
3315 ms
Safe context
262K
Memory
26.6 GB / 92.2 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 | 58.4 tok/s | 1808 ms | 262K |
| Coding | A | Runs well | 54.3 tok/s | 3563 ms | 262K |
| Agentic Coding | A | Runs well | 58.4 tok/s | 4821 ms | 262K |
| Reasoning | A | Runs well | 58.4 tok/s | 3917 ms | 262K |
| RAG | A | Runs well | 58.4 tok/s | 6027 ms | 262K |
How Ministral 3 14B (14B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A74 |
Q3_K_S | 3 | 6.9 GB | Low | A74 |
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 |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 5.8 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Mac Studio M2 Ultra 128GB can run Ministral 3 14B with a A grade (Runs well). Expected decode speed: 54.3 tok/s.
Ministral 3 14B (14B parameters) requires approximately 26.6 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 Mac Studio M2 Ultra 128GB, Ministral 3 14B achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.
For coding workloads, Ministral 3 14B on Mac Studio M2 Ultra 128GB receives a A grade with 54.3 tok/s and 262K context.
On Mac Studio M2 Ultra 128GB, Ministral 3 14B can safely use up to 262K 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-m2-ultra-128gb" 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 |
| A74 |
Q4_K_M | 4 | 8.5 GB | Medium | A74 |
Q5_K_M | 5 | 10.1 GB | High | A75 |
Q6_K | 6 | 11.5 GB | High | A75 |
Q8_0 | 8 | 15.0 GB | Very High | A75 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | A77 |
| 70.2 tok/s |
| Qwen 3.5 27B | 27B | S | 30.4 tok/s |
| Qwen 3.6 27B | 27B | S | 30.5 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 16.9 tok/s |
Not always. Mac Studio M2 Ultra 128GB 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.