Can Vicuna 7B run on GTX 1080 Ti 11GB?

YES — With Q2_K

C42Usable

Estimated from fit model

Vicuna 7B needs ~12.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~46 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

Vicuna 7B at Q4_K_M needs 14.4 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q2_K (12.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.4 GB, exceeds 11.0 GB available

14.4 GB required11.0 GB available

131% VRAM needed

3.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

27.1 tok/s

TTFT

7151 ms

Safe context

Memory

14.4 GB / 11.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsVicuna 7B 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: 27.1 tok/s decode · 7.2s TTFT (warm) · 68 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.

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

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 0.4 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	C	Runs with offload	66.9 tok/s	1579 ms	4K
Coding	F	Too heavy	27.1 tok/s	7151 ms	4K
Agentic Coding	F	Too heavy	10.4 tok/s	26969 ms	4K
Reasoning	F	Too heavy	27.1 tok/s	8451 ms	4K
RAG	F	Too heavy	10.4 tok/s	33712 ms	4K

Quantization options

How Vicuna 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C50
Q3_K_S	3	3.4 GB	Low	C51
NVFP4	4	3.9 GB	Medium	C52
Q4_K_M	4	4.3 GB	Medium	C53
Q5_K_M	5	5.0 GB	High	C53
Q6_K	6	5.7 GB	High	C53
Q8_0Best for your GPU	8	7.5 GB	Very High	C52
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Vicuna 7B on your machine.

Run

ollama run vicuna

Opções de upgrade

Hardware que roda bem Vicuna 7B

RTX 5060 Ti 16GBOpção econômica

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBMelhor custo-benefício

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

RTX 2000 Ada 16GBUpgrade NVIDIA

16 GB VRAM (+5)

Makes the model fit on the accelerator instead of staying completely out of reach.51.3 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$625 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Vicuna 7B?

Yes, GTX 1080 Ti 11GB can run Vicuna 7B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 14.4 GB which exceeds available memory, but at Q2_K it needs only 12.8 GB. Expected decode speed: 46.2 tok/s.

How much VRAM does Vicuna 7B need?

Vicuna 7B (7B parameters) requires approximately 14.4 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q2_K using 12.8 GB.

What is the best quantization for Vicuna 7B?

The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q2_K, which uses 12.8 GB.

What speed will Vicuna 7B run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Vicuna 7B achieves approximately 46.2 tokens per second decode speed with a time-to-first-token of 4194ms using Q2_K quantization.

Can GTX 1080 Ti 11GB run Vicuna 7B for coding?

For coding workloads, Vicuna 7B on GTX 1080 Ti 11GB receives a F grade with 27.1 tok/s and 4K context.

What context window can Vicuna 7B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Vicuna 7B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if Vicuna 7B feels slow on GTX 1080 Ti 11GB?

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 GTX 1080 Ti 11GB See all hardware for Vicuna 7B

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