Ministral 3 8B needs ~10.9 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~70 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
69.5 tok/s
TTFT
2787 ms
Safe context
24K
Memory
10.9 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 69.5 tok/s | 1520 ms | 24K |
| Coding | A | Tight fit | 69.5 tok/s | 2787 ms | 24K |
| Agentic Coding | F | Too heavy | 43.5 tok/s | 6474 ms | 24K |
| Reasoning | A | Tight fit | 69.5 tok/s | 3294 ms | 24K |
| RAG | F | Too heavy | 43.5 tok/s | 8093 ms | 24K |
How Ministral 3 8B (8B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 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 | 4.5 GB | Medium | A82 |
Q4_K_M | 4 | 4.9 GB | Medium | A82 |
Q5_K_M | 5 | 5.8 GB | High | A83 |
Q6_K | 6 | 6.6 GB | High | A83 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A82 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 99Yes, RTX 4000 Ada Laptop 12GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 69.5 tok/s.
Ministral 3 8B (8B parameters) requires approximately 10.9 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 RTX 4000 Ada Laptop 12GB, Ministral 3 8B achieves approximately 69.5 tokens per second decode speed with a time-to-first-token of 2787ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on RTX 4000 Ada Laptop 12GB receives a A grade with 69.5 tok/s and 24K context.
On RTX 4000 Ada Laptop 12GB, Ministral 3 8B can safely use up to 24K 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-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: