Llama 3.2 11B Vision Instruct — VRAM, speed & local setup
Written by Jakub Rusinowski · Last updated
Meta's first open multimodal model. Understands images and text together — describe photos, analyze charts, read documents. Runs on 8GB+ VRAM GPUs. Listed in full on the dedicated Llama 3.2 Vision page, which is the canonical entry for this model and carries the install command.
Llama 3.2 11B Vision Instruct needs about 7 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.
VRAM and speed by quantization
Quoted against NVIDIA RTX 4090 (24 GB). Includes the KV cache at 8K context, so it reads higher than the headline figure.
| Quant | Memory | VRAM | Speed (est.) | Fit |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 5.6 GB | ~129 tok/s | Fits | |
| Q3_K_M 3.41 bpw | 6.7 GB | ~110 tok/s | Fits | |
| Q4_K_M 4.83 bpw | 8.5 GB | ~86 tok/s | Fits | |
| Q5_K_M 5.67 bpw | 9.7 GB | ~77 tok/s | Fits | |
| Q6_K 6.56 bpw | 10.8 GB | ~68 tok/s | Fits | |
| Q8_0 8.50 bpw | 13.4 GB | ~55 tok/s | Fits | |
| F16 16.00 bpw | 23.3 GB | ~32 tok/s | Tight |
Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Llama 3.2 11B Vision Instruct VRAM calculator →
Get Llama 3.2 11B Vision Instruct running
The cheapest catalogued GPU that runs Llama 3.2 11B Vision Instruct is the Intel Arc B570 (10 GB).
or compare on Vast.ai from $0.35/hr (typical low · varies)
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How to run Llama 3.2 11B Vision Instruct
Install Ollama, then run:
ollama run llama-3-2Specifications
Curated — A hand-written entry from before this catalogue recorded its sources. The figures are long-standing but their provenance is not on file.
- Parameters
- 11 Billion
- Context window
- 128,000
- Architecture
- Dense + Vision Encoder
- Provider
- Meta
- Licence
- Llama Community
- Practical deployment
- Laptop
- Specified at
- Q4_K_M
- System RAM
- 16 GB
- Record updated
- 2026-08-15
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Quality and use cases
Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.
Can I run Llama 3.2 11B Vision Instruct on my GPU?
- Llama 3.2 11B Vision Instruct on AMD Radeon RX 9060 XT 8GB
- Llama 3.2 11B Vision Instruct on AMD Ryzen AI Max+ 395
- Llama 3.2 11B Vision Instruct on Apple M1 Ultra
- Llama 3.2 11B Vision Instruct on Apple M2 Max
- Llama 3.2 11B Vision Instruct on Apple M4 Max
- Llama 3.2 11B Vision Instruct on Apple M5 Max
- Llama 3.2 11B Vision Instruct on Intel Arc B570
- Llama 3.2 11B Vision Instruct on Intel Arc B580
- Llama 3.2 11B Vision Instruct on NVIDIA A100 80GB (PCIe)
- Llama 3.2 11B Vision Instruct on NVIDIA DGX Spark
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 3060 (12GB)
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 3070
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 3070 Ti
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 3080 (10GB)
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 4060
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 4070
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 4070 Super
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 4070 Ti
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 5060
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 5060 Ti 8GB
- Llama 3.2 11B Vision Instruct on NVIDIA GeForce RTX 5070
- Llama 3.2 11B Vision Instruct on NVIDIA H100 80GB (PCIe)
- Llama 3.2 11B Vision Instruct on NVIDIA RTX PRO 6000 Blackwell
Other Llama 3.2 Family sizes
Llama 3.2 11B Vision Instruct — frequently asked questions
How much VRAM does Llama 3.2 11B Vision Instruct need?
About 7 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Llama 3.2 11B Vision Instruct run on an RTX 4090 (24 GB)?
Yes. Llama 3.2 11B Vision Instruct needs about 7 GB at Q4_K_M, inside a 24 GB card, at an estimated 86 tokens/sec.
How do I run Llama 3.2 11B Vision Instruct locally?
Install Ollama and run `ollama run llama-3-2`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Llama 3.2 Family come in?
Llama 3.2 1B Instruct (2 GB), Llama 3.2 3B Instruct (3 GB), Llama 3.2 11B Vision Instruct (7 GB), Llama 3.2 90B Vision Instruct (54 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.