Can Llama 3.2 11B Vision run on RTX 4000 Ada 20GB?

YES — Runs Great

B67Good

Estimated from fit model

Llama 3.2 11B Vision needs ~11.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 11.9 GB, 45.0 tok/s, Runs well

11.9 GB required20.0 GB available

60% VRAM used

Fit status

Runs well

Decode

45.0 tok/s

TTFT

4304 ms

Safe context

16K

Memory

11.9 GB / 20.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on RTX 4000 Ada 20GB
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: 45.0 tok/s decode · 4.3s TTFT (warm) · 113 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	45.0 tok/s	2347 ms	16K
Coding	B	Runs well	45.0 tok/s	4304 ms	16K
Agentic Coding	B	Runs well	45.0 tok/s	6260 ms	16K
Reasoning	B	Runs well	45.0 tok/s	5086 ms	16K
RAG	B	Runs well	45.0 tok/s	7825 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B61
Q3_K_S	3	5.4 GB	Low	B61
NVFP4	4	6.2 GB	Medium	B62
Q4_K_M	4	6.7 GB	Medium	B62
Q5_K_M	5	7.9 GB	High	B63
Q6_K	6	9.0 GB	High	B64
Q8_0Best for your GPU	8	11.8 GB	Very High	B65
F16	16	22.5 GB	Maximum	F0

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 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 133%.105 tok/s decode

Raises estimated decode speed by about 133%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 173%.122.7 tok/s decode

Raises estimated decode speed by about 173%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 101%.90.4 tok/s decode

Raises estimated decode speed by about 101%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Llama 3.2 11B Vision?

Yes, RTX 4000 Ada 20GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 45.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.9 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Llama 3.2 11B Vision achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4304ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on RTX 4000 Ada 20GB receives a B grade with 45.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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.

See all results for RTX 4000 Ada 20GB See all hardware for Llama 3.2 11B Vision

Embed this result▼

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<iframe src="https://willitrunai.com/embed/llama-3.2-11b-vision-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

45.0 tok/s

2347 ms

16K

Coding

Runs well

45.0 tok/s

4304 ms

16K

Agentic Coding

Runs well

45.0 tok/s

6260 ms

16K

Reasoning

Runs well

45.0 tok/s

5086 ms

16K

RAG

Runs well

45.0 tok/s

7825 ms

16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B61
Q3_K_S	3	5.4 GB	Low	B61
NVFP4	4	6.2 GB	Medium	B62
Q4_K_M	4	6.7 GB	Medium	B62
Q5_K_M	5	7.9 GB	High	B63
Q6_K	6	9.0 GB	High	B64
Q8_0Best for your GPU	8	11.8 GB	Very High	B65
F16	16	22.5 GB	Maximum	F0

Frequently asked questions

Can RTX 4000 Ada 20GB run Llama 3.2 11B Vision?

Yes, RTX 4000 Ada 20GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 45.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.9 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Llama 3.2 11B Vision achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4304ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on RTX 4000 Ada 20GB receives a B grade with 45.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, 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.