Can Llama 3.2 11B Vision run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if Llama 3.2 11B Vision feels slow on RTX 4000 Ada Laptop 12GB?

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

YES — Tight Fit

B66Good

Estimated from fit model

Llama 3.2 11B Vision needs ~11.1 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 11.1 GB, 50.5 tok/s, Tight fit

11.1 GB required12.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

50.5 tok/s

TTFT

3832 ms

Safe context

16K

Memory

11.1 GB / 12.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on RTX 4000 Ada Laptop 12GB
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: 50.5 tok/s decode · 3.8s TTFT (warm) · 126 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.

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	50.5 tok/s	2090 ms	16K
Coding	B	Tight fit	47.0 tok/s	4119 ms	16K
Agentic Coding	C	Very compromised (needs ~0.5 GB host RAM)	31.9 tok/s	8819 ms	16K
Reasoning	B	Tight fit	50.5 tok/s	4529 ms	16K
RAG	C	Very compromised (needs ~0.5 GB host RAM)	31.9 tok/s	11023 ms

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B65
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 (+4)448 GB/s (+16)

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 (+4)

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 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)896 GB/s (+464)

Raises estimated decode speed by about 82%.91.9 tok/s decode

Raises estimated decode speed by about 82%.

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

~$749 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Llama 3.2 11B Vision?

Yes, RTX 4000 Ada Laptop 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 47.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.1 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 Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Llama 3.2 11B Vision achieves approximately 47.0 tokens per second decode speed with a time-to-first-token of 4119ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on RTX 4000 Ada Laptop 12GB receives a B grade with 47.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, 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 Laptop 12GB See all hardware for Llama 3.2 11B Vision

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