text model · Llama 3.2 Vision · Windows

Can I run Llama 3.2 Vision 11B on 32GB RAM Laptop (CPU/iGPU only)?

Compatibility verdict VRAM threshold engine

Yes, it runs slow on this hardware ~6 tok/s est.

Yes. Llama 3.2 Vision 11B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~9 GB of ~28 GB usable).

Needs ~9 GB Device usable ~28 GB

Runs at Q4_K_M using ~9 GB of ~28 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. 32GB RAM Laptop (CPU/iGPU only) leaves ~19 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed: ~9 GB
Usable on device: ~28 GB
Device memory: 32 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~28 GB usable

Q2_K

~6.1 GB

Q3_K_M

~6.8 GB

Q4_K_M

~9 GB

Q5_K_M

~9.2 GB

Q6_K

~10.4 GB

Q8_0

~13.1 GB

FP16

~23 GB

The line marks 32GB RAM Laptop (CPU/iGPU only)'s ~28 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~28 W
Electricity / 1M tokens: ~$0.19
Pays for itself after: ~3,548M tok

At ~$0.15/kWh and the estimated ~6 tok/s, a million generated tokens costs about $0.19 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,100 32GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 3,548 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run llama3.2-vision:11b

llama.cpp

$ llama-cli -hf leafspark/Llama-3.2-11B-Vision-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get leafspark/Llama-3.2-11B-Vision-Instruct-GGUF

Model Llama 3.2 Vision

Parameters: 10.7B
Q4_K_M size: 7.36 GB
Q8_0 size: 11.49 GB
Context: 128k
Ollama tag: llama3.2-vision:11b

Full Llama 3.2 Vision 11B requirements →

Device Windows

Memory: 32 GB ram
Usable for weights: ~28 GB
Power draw: ~28 W
Best runtime: Ollama (llama.cpp backend)

Best models for 32GB RAM Laptop (CPU/iGPU only) →

You could also run

Run Llama 3.2 Vision 11B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Apple M2 (16GB)Apple M4 (16GB)Nvidia GeForce RTX 4060 Ti (16GB)Nvidia GeForce RTX 4080 (16GB)

FAQ

Can 32GB RAM Laptop (CPU/iGPU only) run Llama 3.2 Vision 11B?

Yes. Llama 3.2 Vision 11B runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~9 GB of ~28 GB usable).

How much memory does Llama 3.2 Vision 11B need?

32GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.

What is the best tool to run Llama 3.2 Vision 11B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Markdown

[![Llama 3.2 Vision 11B on 32GB RAM Laptop (CPU/iGPU only)](https://localmodel.run/badge/llama-3.2-vision-11b/laptop-32gb.svg)](https://localmodel.run/can-i-run/llama-3.2-vision-11b/laptop-32gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.