Ministral 3 14B needs ~15.0 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~44 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
43.5 tok/s
TTFT
4451 ms
Safe context
23K
Memory
15.0 GB / 16.0 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | S | Tight fit | 40.5 tok/s | 2610 ms | 23K |
| Coding | S | Tight fit | 43.5 tok/s | 4451 ms | 23K |
| Agentic Coding | F | Too heavy | 26.3 tok/s | 10703 ms | 23K |
| Reasoning | S | Tight fit | 43.5 tok/s | 5261 ms | 23K |
| RAG | F | Too heavy | 26.3 tok/s | 13379 ms | 23K |
How Ministral 3 14B (14B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A84 |
Q3_K_S | 3 | 6.9 GB | Low | S86 |
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 99Yes, Tesla P100 16GB can run Ministral 3 14B with a S grade (Tight fit). Expected decode speed: 43.5 tok/s.
Ministral 3 14B (14B parameters) requires approximately 15.0 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 Tesla P100 16GB, Ministral 3 14B achieves approximately 43.5 tokens per second decode speed with a time-to-first-token of 4451ms using Q4_K_M quantization.
For coding workloads, Ministral 3 14B on Tesla P100 16GB receives a S grade with 43.5 tok/s and 23K context.
On Tesla P100 16GB, Ministral 3 14B 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-3-14b-on-tesla-p100-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| S87 |
Q4_K_M | 4 | 8.5 GB | Medium | S86 |
Q5_K_M | 5 | 10.1 GB | High | S86 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | S86 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 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.