Will It Run AI

Can Ministral 8B run on RTX 3060 12GB?

YES — Runs Great

B64Good
Estimated from fit model

Ministral 8B needs ~9.5 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.5 GB, 52.3 tok/s, Runs well
9.5 GB required12.0 GB available
79% VRAM used

Fit status

Runs well

Decode

52.3 tok/s

TTFT

3699 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 3060 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: 52.3 tok/s decode · 3.7s TTFT (warm) · 131 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well52.3 tok/s2018 ms34K
CodingBRuns well48.7 tok/s3976 ms34K
Agentic CodingBRuns with offload52.3 tok/s5380 ms34K
ReasoningBRuns well52.3 tok/s4371 ms34K
RAGBRuns with offload52.3 tok/s6725 ms34K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB59
Q3_K_S
3
3.9 GB
LowB60
NVFP4
4
4.5 GB
MediumB61
Q4_K_M
4
4.9 GB
MediumB61
Q5_K_M
5
5.8 GB
HighB62
Q6_K
6
6.6 GB
HighB62
Q8_0Best for your GPU
8
8.6 GB
Very HighB61
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Ministral 8B on your machine.

Run

ollama run ministral

Opciones de mejora

Hardware que ejecuta bien Ministral 8B

NVIDIARTX 5070 Ti 16GBOpción económica
16 GB VRAM (+4)896 GB/s (+536)
B
Sube la velocidad estimada de decodificación alrededor de un 114%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 114%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$749 MSRP

NVIDIARTX 4070 Ti Super 16GBMejor relación calidad-precio
16 GB VRAM (+4)672 GB/s (+312)
B
Sube la velocidad estimada de decodificación alrededor de un 114%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 114%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$799 MSRP

NVIDIARTX 4080 Super 16GBMejora NVIDIA
16 GB VRAM (+4)736 GB/s (+376)
B
Sube la velocidad estimada de decodificación alrededor de un 114%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 114%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$999 MSRP

Frequently asked questions

Can RTX 3060 12GB run Ministral 8B?

Yes, RTX 3060 12GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 48.7 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 3060 12GB?

On RTX 3060 12GB, Ministral 8B achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3976ms using Q4_K_M quantization.

Can RTX 3060 12GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RTX 3060 12GB receives a B grade with 48.7 tok/s and 34K context.

What context window can Ministral 8B use on RTX 3060 12GB?

On RTX 3060 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.

See all results for RTX 3060 12GBSee all hardware for Ministral 8B
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