Can Ministral 3 14B run on RTX 5070 12GB?

BARELY — Tight on Memory

A75Great

Estimated from fit model

Ministral 3 14B needs ~14.0 GB VRAM. RTX 5070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 14.0 GB, 29.8 tok/s, Very compromised (needs ~1.2 GB host RAM)

14.0 GB required12.0 GB available

117% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.2 GB host RAM)

Decode

29.8 tok/s

TTFT

6489 ms

Safe context

Memory

14.0 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.8 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on RTX 5070 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: 29.8 tok/s decode · 6.5s TTFT (warm) · 75 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.5 GB host RAM)	36.0 tok/s	2937 ms	4K
Coding	A	Very compromised (needs ~1.2 GB host RAM)	29.8 tok/s	6489 ms	4K
Agentic Coding	F	Too heavy	21.5 tok/s	13106 ms	4K
Reasoning	A	Very compromised (needs ~1.2 GB host RAM)	29.8 tok/s	7668 ms	4K
RAG	F	Too heavy	21.5 tok/s	16382 ms	4K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on RTX 5070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	S88
Q3_K_S	3	6.9 GB	Low	S87
NVFP4	4	7.8 GB	Medium	S87
Q4_K_MBest for your GPU	4	8.5 GB	Medium	S87
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 3 14B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \
  --hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 5070 12GB run Ministral 3 14B?

Yes, RTX 5070 12GB can run Ministral 3 14B with a A grade (Very compromised (needs ~1.2 GB host RAM)). Expected decode speed: 29.8 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 14.0 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 5070 12GB?

On RTX 5070 12GB, Ministral 3 14B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6489ms using Q4_K_M quantization.

Can RTX 5070 12GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on RTX 5070 12GB receives a A grade with 29.8 tok/s and 4K context.

What context window can Ministral 3 14B use on RTX 5070 12GB?

On RTX 5070 12GB, 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 5070 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 5070 12GB See all hardware for Ministral 3 14B

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