Can Ministral 8B run on GTX 1080 Ti 11GB?

YES — Tight Fit

B62Good

Estimated from fit model

Ministral 8B needs ~9.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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) — 9.4 GB, 62.9 tok/s, Tight fit

9.4 GB required11.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

62.9 tok/s

TTFT

3078 ms

Safe context

28K

Memory

9.4 GB / 11.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsMinistral 8B on GTX 1080 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: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 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	B	Runs well	62.9 tok/s	1679 ms	28K
Coding	B	Tight fit	58.5 tok/s	3308 ms	28K
Agentic Coding	B	Runs with offload (needs ~0.2 GB host RAM)	41.0 tok/s	6863 ms	28K
Reasoning	B	Tight fit	62.9 tok/s	3637 ms	28K
RAG	B	Runs with offload (needs ~0.2 GB host RAM)	41.0 tok/s	8579 ms

Quantization options

How Ministral 8B (8B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B60
Q3_K_S	3	3.9 GB	Low	B61
NVFP4	4

Get started

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

Run

ollama run ministral

Upgrade options

Hardware that runs Ministral 8B well

RTX 3060 12GBBudget pick

12 GB VRAM (+1)

This setup is broadly balanced for this model.52.3 tok/s decode

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.61.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.46.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Ministral 8B?

Yes, GTX 1080 Ti 11GB can run Ministral 8B with a B grade (Tight fit). Expected decode speed: 58.5 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 8B?

The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 8B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Ministral 8B achieves approximately 58.5 tokens per second decode speed with a time-to-first-token of 3308ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on GTX 1080 Ti 11GB receives a B grade with 58.5 tok/s and 28K context.

What context window can Ministral 8B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Ministral 8B can safely use up to 28K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for Ministral 8B

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<iframe src="https://willitrunai.com/embed/ministral-8b-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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