How much VRAM does Llama 3.2 11B Vision Instruct need?

About 7.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 12.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.

Will Llama 3.2 11B Vision Instruct run on a 10GB GPU?

Yes — at Q4_K_M and 4K context it needs about 7.7 GB, which leaves 2.3 GB spare on a RTX 3080 10G. That is the smallest card in this index that clears it comfortably; 56 of 63 do.

Which quantization of Llama 3.2 11B Vision Instruct should I use?

Q8_0 has the lowest published quality loss (0.5%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, Q8_0.

Llama 3.2 11B Vision Instruct

11B

Meta Llama 3.2

Multimodal Llama with image understanding. Vision encoder adds ~2GB VRAM overhead.

Consumer GPUMac / Apple Silicon

131K

Max Context

Quant Variants

GGUF Q8_0

Best Quality

99.5%

Accuracy Retained

Calculate VRAM Hugging Face Compare

Quantization Variants

Per-quant VRAM, quality loss, and inference speed on RTX 4090

Measured = site benchmarks · Estimated = formula · Community = public reports

Format	Level	BPW	VRAM	PPL Loss	Speed	Source	Actions
GGUF	Q4_K_M	4.85	9.5 GB	3.5%	88 tok/s	Estimated	Calc HF
GGUF	Q8_0	8.5	14.2 GB	0.5%	72 tok/s	Estimated	Calc HF

Similar models

Compare with Llama 3.2

Llama 3.2 3B Instruct

Meta Llama 3.2

Consumer GPUMac / Apple Silicon

2.0 GBmin VRAM99.8%accuracy

Tiny but capable. Runs on 4GB VRAM or 8GB RAM, even on phones via llama.cpp.

Llama 3.2 1B Instruct

Meta Llama 3.2

Consumer GPUMac / Apple Silicon

1.0 GBmin VRAM99.5%accuracy

Ultra-light Llama for mobile and embedded. Sub-2GB VRAM with Q4.

90B

Llama 3.2 90B Vision Instruct

Meta Llama 3.2

Pro GPU

44.2 GBmin VRAM97.2%accuracy

Flagship multimodal Llama. Requires dual 4090 or A100; vision adds ~3GB overhead.

12B

Gemma 3 12B IT

Google Gemma 3

Consumer GPUMac / Apple Silicon

8.0 GBmin VRAM98.7%accuracy

Mid-size Gemma 3 with vision. Fits 16GB at Q4; excellent multilingual chat.

Running Llama 3.2 11B Vision Instruct locally

At Q4_K_M and 4K of context, Llama 3.2 11B Vision Instruct needs about 7.7 GB — 6.3 GB of weights, 0.63 GB of KV cache and a 0.7 GB activation buffer. The smallest card in this index that clears that comfortably is the RTX 3080 10G at 10 GB, and 56 of the 63 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.

What longer context costs

Going from 4K to 32K adds about 4.37 GB, taking the total to 12.5 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 29 GB.

Which build to download

This index only tracks Llama 3.2 11B Vision Instruct in GGUF, across 2 levels (Q4_K_M, Q8_0). Q8_0 carries the lowest published perplexity loss at 0.5%. The fastest level measured here is Q4_K_M at 88 tok/s on an RTX 4090, batch 1. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.

Common questions

How much VRAM does Llama 3.2 11B Vision Instruct need?: About 7.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 12.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
Will Llama 3.2 11B Vision Instruct run on a 10GB GPU?: Yes — at Q4_K_M and 4K context it needs about 7.7 GB, which leaves 2.3 GB spare on a RTX 3080 10G. That is the smallest card in this index that clears it comfortably; 56 of 63 do.
Which quantization of Llama 3.2 11B Vision Instruct should I use?: Q8_0 has the lowest published quality loss (0.5%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, Q8_0.

Where this model fits

Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.

Runs comfortably on: RTX 3080 10G10GB · +2.3 RTX 507012GB · +4.3 RTX 4070 Ti12GB · +4.3 RTX 4070 Super12GB · +4.3 RTX 407012GB · +4.3
Tight but possible: RTX 5060 Ti 8G8GB RTX 50608GB RTX 4060 Ti 8G8GB
Just misses: Mac M3 8G+1.7GB over
Ships in: GGUF
Try it yourself: Size it in the calculator Generate the run command

How to actually run this

Deployment guides for this model and this class of hardware.

This page's figures change when the model or the runtime does.Last updated 2026-09-21 RSS → /feed.xml