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Llama 3.2 11B Vision (11B parameters) requires approximately 10.5 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 13 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Llama 3.2 11B Vision on your machine.
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ollama run llama3.2-vision:11bQuick specs
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No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | — |
Q3_K_S | 3 | 5.4 GB | Low | — |
NVFP4 | 4 | 6.2 GB | Medium | — |
Q4_K_M | 4 | 6.7 GB | Medium | — |
Q5_K_M | 5 | 7.9 GB | High | — |
Q6_K | 6 | 9.0 GB | High | — |
Q8_0 | 8 | 11.8 GB | Very High | — |
F16 | 16 | 22.5 GB | Maximum | — |
Quality benchmarks
Reasoning
Source: official · 2024-09-25
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
Llama 3.2 11B Vision (11B parameters) requires approximately 10.5 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc B580 12GB can run Llama 3.2 11B Vision with a compatibility score of 65/100. It provides 12 GB of memory and achieves approximately 35.1 tokens per second.
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for Llama 3.2 11B Vision: RTX 4080 Super 16GB (score: 71/100), RTX 5070 Ti 16GB (score: 71/100), RTX 5080 16GB (score: 71/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, Llama 3.2 11B Vision is well-suited for chat as well as vision. It was designed with these use cases in mind.
See also
