RTX 5070 Ti 16GBコスパ重視16 GB VRAM (+4)896 GB/s (+608)
B
Raises estimated decode speed by about 126%.112 tok/sデコード
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
〜$749 MSRP
Can Ministral 8B run on RTX A2000 12GB?
YES — Runs Great
B64Good
Estimated from fit model
Ministral 8B needs ~9.5 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~50 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.5 GB, 49.5 tok/s, Runs well
9.5 GB required12.0 GB available
79% VRAM used
Fit status
Runs well
Decode
49.5 tok/s
TTFT
3912 ms
Safe context
34K
Memory
9.5 GB / 12.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsMinistral 8B on RTX A2000 12GB
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: 49.5 tok/s decode · 3.9s TTFT (warm) · 124 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 49.5 tok/s | 2134 ms | 34K |
| Coding | B | Runs well | 49.5 tok/s | 3912 ms | 34K |
| Agentic Coding | B | Runs with offload | 49.5 tok/s | 5691 ms | 34K |
| Reasoning | B | Runs well | 49.5 tok/s | 4624 ms | 34K |
| RAG | B | Runs with offload | 49.5 tok/s | 7113 ms | 34K |
Quantization options
How Ministral 8B (8B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B59 |
Q3_K_S | 3 | 3.9 GB | Low | B60 |
NVFP4 | 4 | 4.5 GB | Medium | B61 |
Q4_K_M | 4 | 4.9 GB | Medium | B61 |
Q5_K_M | 5 | 5.8 GB | High | B62 |
Q6_K | 6 | 6.6 GB | High | B62 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | B61 |
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 5070 Ti 16GBコスパ重視RTX 4070 Ti Super 16GBコスパ最良16 GB VRAM (+4)672 GB/s (+384)
B
Raises estimated decode speed by about 126%.112 tok/sデコード
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
〜$799 MSRP
RTX 4080 Super 16GBNVIDIAアップグレード16 GB VRAM (+4)736 GB/s (+448)
B
Raises estimated decode speed by about 126%.112 tok/sデコード
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
〜$999 MSRP
Frequently asked questions
Can RTX A2000 12GB run Ministral 8B?
Yes, RTX A2000 12GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 49.5 tok/s.
How much VRAM does Ministral 8B need?
Ministral 8B (8B parameters) requires approximately 9.5 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 A2000 12GB?
On RTX A2000 12GB, Ministral 8B achieves approximately 49.5 tokens per second decode speed with a time-to-first-token of 3912ms using Q4_K_M quantization.
Can RTX A2000 12GB run Ministral 8B for coding?
For coding workloads, Ministral 8B on RTX A2000 12GB receives a B grade with 49.5 tok/s and 34K context.
What context window can Ministral 8B use on RTX A2000 12GB?
On RTX A2000 12GB, Ministral 8B can safely use up to 34K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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