RTX 3060 12GB经济之选12 GB VRAM (+2)
B
This setup is broadly balanced for this model.52.3 tok/s 解码
~$329 MSRP
Can Ministral 8B run on RTX 3080 10GB?
YES — Tight Fit
B63Good
Estimated from fit model
Ministral 8B needs ~9.3 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~112 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 9.3 GB, 112.0 tok/s, Tight fit
9.3 GB required10.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
21K
Memory
9.3 GB / 10.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsMinistral 8B on RTX 3080 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 112.0 tok/s | 943 ms | 21K |
| Coding | B | Tight fit | 112.0 tok/s | 1729 ms | 21K |
| Agentic Coding | C | Very compromised (needs ~0.6 GB host RAM) | 71.4 tok/s | 3942 ms | 21K |
| Reasoning | B | Tight fit | 112.0 tok/s | 2043 ms | 21K |
| RAG | C | Very compromised (needs ~0.6 GB host RAM) | 71.4 tok/s | 4927 ms | 21K |
Quantization options
How Ministral 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B61 |
Q3_K_S | 3 | 3.9 GB | Low | B62 |
NVFP4 | 4 | 4.5 GB | Medium | B62 |
Q4_K_M | 4 | 4.9 GB | Medium | B62 |
Q5_K_M | 5 | 5.8 GB | High | B62 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | B62 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministral升级选项
能流畅运行 Ministral 8B 的硬件
Frequently asked questions
Can RTX 3080 10GB run Ministral 8B?
Yes, RTX 3080 10GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 112.0 tok/s.
How much VRAM does Ministral 8B need?
Ministral 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Ministral 8B?
The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Ministral 8B run at on RTX 3080 10GB?
On RTX 3080 10GB, Ministral 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can RTX 3080 10GB run Ministral 8B for coding?
For coding workloads, Ministral 8B on RTX 3080 10GB receives a B grade with 112.0 tok/s and 21K context.
What context window can Ministral 8B use on RTX 3080 10GB?
On RTX 3080 10GB, Ministral 8B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
What should I upgrade first if Ministral 8B feels slow on RTX 3080 10GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/ministral-8b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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