RTX 3060 12GBOpción económica12 GB VRAM (+1)
B
Este setup está bastante equilibrado para este modelo.52.3 tok/s decodificación
~$329 MSRP
Can Ministral 8B run on RTX 2080 Ti 11GB?
YES — Tight Fit
B63Good
Estimated from fit model
Ministral 8B needs ~9.4 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~82 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, 88.2 tok/s, Tight fit
9.4 GB required11.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
88.2 tok/s
TTFT
2195 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 RTX 2080 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: 88.2 tok/s decode · 2.2s TTFT (warm) · 221 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 | 82.0 tok/s | 1287 ms | 28K |
| Coding | B | Tight fit | 82.0 tok/s | 2360 ms | 28K |
| Agentic Coding | B | Runs with offload | 53.5 tok/s | 5262 ms | 28K |
| Reasoning | B | Tight fit | 82.0 tok/s | 2789 ms | 28K |
| RAG | B | Runs with offload | 53.5 tok/s | 6577 ms | 28K |
Quantization options
How Ministral 8B (8B params) fits at each quantization level on RTX 2080 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 ministralOpciones de mejora
Hardware que ejecuta bien Ministral 8B
RTX 3060 12GBOpción económicaRTX 5060 Ti 16GBMejor relación calidad-precio16 GB VRAM (+5)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.61.2 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
RTX 5070 12GBMejora NVIDIA12 GB VRAM (+1)672 GB/s (+56)
B
Este setup está bastante equilibrado para este modelo.93.3 tok/s decodificación
~$549 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run Ministral 8B?
Yes, RTX 2080 Ti 11GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 82.0 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 RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, Ministral 8B achieves approximately 82.0 tokens per second decode speed with a time-to-first-token of 2360ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run Ministral 8B for coding?
For coding workloads, Ministral 8B on RTX 2080 Ti 11GB receives a B grade with 82.0 tok/s and 28K context.
What context window can Ministral 8B use on RTX 2080 Ti 11GB?
On RTX 2080 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-rtx-2080-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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