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URL: https://willitrunai.com/can-run/llama-3.2-11b-vision-on-rtx-3090-ti-24gb

⇱ Llama 3.2 11B Vision on RTX 3090 Ti 24GB? YES

Can Llama 3.2 11B Vision run on RTX 3090 Ti 24GB?

YES — Runs Great

B67Good
○Estimated from fit model

Llama 3.2 11B Vision needs ~12.3 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~115 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 12.3 GB, 114.7 tok/s, Runs well
12.3 GB required24.0 GB available
51% VRAM used

Fit status

Runs well

Decode

114.7 tok/s

TTFT

1688 ms

Safe context

16K

Memory

12.3 GB / 24.0 GB

Memory breakdown

Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on RTX 3090 Ti 24GB
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: 114.7 tok/s decode · 1.7s TTFT (warm) · 287 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well114.7 tok/s921 ms16K
CodingBRuns well114.7 tok/s1688 ms16K
Agentic CodingBRuns well114.7 tok/s2456 ms16K
ReasoningBRuns well114.7 tok/s1995 ms16K
RAGBRuns well114.7 tok/s3070 ms16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.3 GB
LowB59
Q3_K_S
3
5.4 GB
LowB60
NVFP4
4
6.2 GB
MediumB61
Q4_K_M
4
6.7 GB
MediumB61
Q5_K_M
5
7.9 GB
HighB62
Q6_K
6
9.0 GB
HighB62
Q8_0Best for your GPU
8
11.8 GB
Very HighB64
F16
16
22.5 GB
MaximumF0

Get started

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

Run

ollama run llama3.2-vision:11b

Frequently asked questions

See all results for RTX 3090 Ti 24GBSee all hardware for Llama 3.2 11B Vision