RTX 3060 12GBコスパ重視12 GB VRAM (+1)
B
This setup is broadly balanced for this model.52.3 tok/sデコード
〜$329 MSRP
Can Ministral 8B run on GTX 1080 Ti 11GB?
YES — Tight Fit
B62Good
Estimated from fit model
Ministral 8B needs ~9.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~59 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) — 9.4 GB, 62.9 tok/s, Tight fit
9.4 GB required11.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
62.9 tok/s
TTFT
3078 ms
Safe context
28K
Memory
9.4 GB / 11.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsMinistral 8B on GTX 1080 Ti 11GB
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: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 62.9 tok/s | 1679 ms | 28K |
| Coding | B | Tight fit | 58.5 tok/s | 3308 ms | 28K |
| Agentic Coding | B | Runs with offload (needs ~0.2 GB host RAM) | 41.0 tok/s | 6863 ms | 28K |
| Reasoning | B | Tight fit | 62.9 tok/s | 3637 ms | 28K |
| RAG | B | Runs with offload (needs ~0.2 GB host RAM) | 41.0 tok/s | 8579 ms | 28K |
Quantization options
How Ministral 8B (8B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B60 |
Q3_K_S | 3 | 3.9 GB | Low | B61 |
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アップグレードオプション
Ministral 8Bを快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+5)
B
Adds memory headroom for longer context windows and future model growth.61.2 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレード16 GB VRAM (+5)
B
Adds memory headroom for longer context windows and future model growth.46.3 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run Ministral 8B?
Yes, GTX 1080 Ti 11GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 58.5 tok/s.
How much VRAM does Ministral 8B need?
Ministral 8B (8B parameters) requires approximately 9.4 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 GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Ministral 8B achieves approximately 58.5 tokens per second decode speed with a time-to-first-token of 3308ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run Ministral 8B for coding?
For coding workloads, Ministral 8B on GTX 1080 Ti 11GB receives a B grade with 58.5 tok/s and 28K context.
What context window can Ministral 8B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Ministral 8B can safely use up to 28K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/ministral-8b-on-gtx-1080-ti-11gb" 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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