Can Ministral 3 8B run on RTX 3080 10GB?

YES — With Offload

A84Great

○Estimated from fit model

Ministral 3 8B needs ~9.9 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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) — 9.9 GB, 112.0 tok/s, Runs with offload

9.9 GB required10.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

17K

Memory

9.9 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on RTX 3080 10GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	112.0 tok/s	943 ms	17K
Coding	A	Runs with offload	112.0 tok/s	1729 ms	17K
Agentic Coding	F	Too heavy	59.7 tok/s	4717 ms	17K
Reasoning	A	Runs with offload	112.0 tok/s	2043 ms	17K
RAG	F	Too heavy	59.7 tok/s	5897 ms	17K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A82
Q3_K_S	3	3.9 GB	Low	A83
NVFP4	4

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

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	76.7 tok/s

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for Ministral 3 8B

URL: https://willitrunai.com/can-run/ministral-3-8b-on-rtx-3080-10gb

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