Can Ministral 3 8B run on RTX 4070 12GB?

YES — Tight Fit

A84Great

○Estimated from fit model

Ministral 3 8B needs ~10.9 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~83 tok/s.

Runtime: SGLangCapacity: TightBandwidth: MediumStack: OptimizedBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.9 GB, 83.3 tok/s, Tight fit

10.9 GB required12.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

83.3 tok/s

TTFT

2325 ms

Safe context

24K

Memory

10.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on RTX 4070 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: 83.3 tok/s decode · 2.3s TTFT (warm) · 208 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	S	Runs well	83.3 tok/s	1268 ms	24K
Coding	A	Tight fit	83.3 tok/s	2325 ms	24K
Agentic Coding	F	Too heavy	52.1 tok/s	5401 ms	24K
Reasoning	A	Tight fit	83.3 tok/s	2748 ms	24K
RAG	F	Too heavy	52.1 tok/s	6752 ms	24K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A80
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4	4.5 GB	Medium	A82
Q4_K_M	4	4.9 GB	Medium	A82
Q5_K_M	5	5.8 GB	High	A83
Q6_K	6	6.6 GB	High	A83
Q8_0Best for your GPU	8	8.6 GB	Very High	A82
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
 --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Frequently asked questions

See all results for RTX 4070 12GB See all hardware for Ministral 3 8B

URL: https://willitrunai.com/can-run/ministral-3-8b-on-rtx-4070-12gb

⇱ Ministral 3 8B on RTX 4070 12GB? TIGHT FIT