Can Ministral 3 14B run on RTX 5060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Ministral 3 14B needs ~14.2 GB but RTX 5060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: LowStack: OptimizedBottleneck: Memory capacity

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) — 14.2 GB, exceeds 8.0 GB available

14.2 GB required8.0 GB available

178% VRAM needed

6.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.5 tok/s

TTFT

29578 ms

Safe context

Memory

14.2 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime2.4 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMinistral 3 14B on RTX 5060 8GB
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: 6.5 tok/s decode · 29.6s TTFT (warm) · 16 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.2 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.9 tok/s	13427 ms	4K
Coding	F	Too heavy	6.5 tok/s	29578 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59487 ms	4K
Reasoning	F	Too heavy	6.5 tok/s	34956 ms	4K
RAG	F	Too heavy	4.4 tok/s	79936 ms	4K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on RTX 5060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Ministral 3 14B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.28 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.22 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$625 MSRP

RTX 4090 24GBBiggest leap

24 GB VRAM (+16)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.77.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can RTX 5060 8GB run Ministral 3 14B?

No, Ministral 3 14B requires more memory than RTX 5060 8GB provides.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 14B?

The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 14B run at on RTX 5060 8GB?

On RTX 5060 8GB, Ministral 3 14B achieves approximately 6.5 tokens per second decode speed with a time-to-first-token of 29578ms using Q4_K_M quantization.

Can RTX 5060 8GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on RTX 5060 8GB receives a F grade with 6.5 tok/s and 4K context.

What context window can Ministral 3 14B use on RTX 5060 8GB?

On RTX 5060 8GB, Ministral 3 14B can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Ministral 3 14B feels slow on RTX 5060 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 5060 8GB See all hardware for Ministral 3 14B

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