Can Llama 3.2 11B Vision run on RTX 2080 Ti 11GB?

Q: What should I upgrade first if Llama 3.2 11B Vision feels slow on RTX 2080 Ti 11GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

B67Good

Estimated from fit model

Llama 3.2 11B Vision needs ~11.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 11.0 GB, 64.1 tok/s, Runs with offload

11.0 GB required11.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

64.1 tok/s

TTFT

3018 ms

Safe context

16K

Memory

11.0 GB / 11.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision 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: 64.1 tok/s decode · 3.0s TTFT (warm) · 160 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.

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

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	B	Tight fit	59.7 tok/s	1770 ms	16K
Coding	B	Runs with offload	59.7 tok/s	3244 ms	16K
Agentic Coding	C	Very compromised	30.6 tok/s	9205 ms	16K
Reasoning	B	Runs with offload	59.7 tok/s	3834 ms	16K
RAG	C	Very compromised	30.6 tok/s	11507 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B66
Q3_K_S	3	5.4 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.44.5 tok/s decode

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

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.33.7 tok/s decode

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

~$499 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.67.8 tok/s decode

~$549 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Llama 3.2 11B Vision?

Yes, RTX 2080 Ti 11GB can run Llama 3.2 11B Vision with a B grade (Runs with offload). Expected decode speed: 59.7 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 11B Vision run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Llama 3.2 11B Vision achieves approximately 59.7 tokens per second decode speed with a time-to-first-token of 3244ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on RTX 2080 Ti 11GB receives a B grade with 59.7 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.2 11B Vision feels slow on RTX 2080 Ti 11GB?

See all results for RTX 2080 Ti 11GB See all hardware for Llama 3.2 11B Vision

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