Can Ministral 3 8B run on RTX 2080 Ti 11GB?

YES — Tight Fit

A84Great

○Estimated from fit model

Ministral 3 8B needs ~10.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~88 tok/s.

Runtime: TransformersCapacity: TightBandwidth: 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) — 10.0 GB, 88.2 tok/s, Tight fit

10.0 GB required11.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

88.2 tok/s

TTFT

2195 ms

Safe context

23K

Memory

10.0 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.8 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on RTX 2080 Ti 11GB
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: 88.2 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	88.2 tok/s	1197 ms	23K
Coding	A	Tight fit	88.2 tok/s	2195 ms	23K
Agentic Coding	A	Very compromised (needs ~0.5 GB host RAM)	51.5 tok/s	5469 ms	23K
Reasoning	A	Tight fit	88.2 tok/s	2594 ms	23K
RAG	A	Very compromised (needs ~0.5 GB host RAM)	51.5 tok/s	6837 ms

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	A82
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 2080 Ti 11GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 9B	9B	S	78.4 tok/s

Frequently asked questions

See all results for RTX 2080 Ti 11GB See all hardware for Ministral 3 8B

URL: https://willitrunai.com/can-run/ministral-3-8b-on-rtx-2080-ti-11gb

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