Ministral 3 8B needs ~10.7 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~112 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
112.0 tok/s
TTFT
1729 ms
Safe context
26K
Memory
10.7 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 | 112.0 tok/s | 943 ms | 26K |
| Coding | A | Tight fit | 112.0 tok/s | 1729 ms | 26K |
| Agentic Coding | F | Too heavy | 79.0 tok/s | 3564 ms | 26K |
| Reasoning | A | Tight fit | 112.0 tok/s | 2043 ms | 26K |
| RAG | F | Too heavy | 79.0 tok/s | 4456 ms | 26K |
How Ministral 3 8B (8B params) fits at each quantization level on RTX 3080 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 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 108.6 tok/s |
Yes, RTX 3080 12GB can run Ministral 3 8B with a A grade (Tight fit). Expected decode speed: 112.0 tok/s.
Ministral 3 8B (8B parameters) requires approximately 10.7 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 3080 12GB, Ministral 3 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on RTX 3080 12GB receives a A grade with 112.0 tok/s and 26K context.
On RTX 3080 12GB, Ministral 3 8B can safely use up to 26K 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-3080-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: