FitMyLLM
Meta/Dense

MetaLlama-3.2-11B-Vision-Instruct

Llama 3.2 11B Vision Instruct — instruction-tuned for visual Q&A tasks.

visionchatThinkingDistilled
11B
Parameters
128K
Context length
14
Benchmarks
10
Quantizations
1.5M
HF downloads
Architecture
Dense
Released
2024-09-25
Layers
32
KV Heads
8
Head Dim
128
Family
llama

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
7.5 GB
6.0 + 1.5 KV
low
Q3_K_L4.3
7.9 GB
6.4 + 1.5 KV
moderate
IQ4_XS4.46
8.1 GB
6.6 + 1.5 KV
moderate
Q4_K_S4.67
8.4 GB
6.9 + 1.5 KV
moderate
Q4_K_M4.89
8.7 GB
7.2 + 1.5 KV
good
Q5_K_S5.57
9.6 GB
8.1 + 1.5 KV
good
Q5_K_M5.7
9.8 GB
8.3 + 1.5 KV
good
Q6_K6.56
11.0 GB
9.5 + 1.5 KV
excellent
Q8_08.5
13.7 GB
12.2 + 1.5 KV
lossless
FP1616
24.0 GB
22.5 + 1.5 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN LLAMA-3.2-11B-VISION-INSTRUCT NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

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Benchmarks (14)

MMBench76.8
IFEval70.0
MMLU-PRO55.0
BBH55.0
MMMU50.7
HumanEval45.0
MATH35.0
GPQA35.0
GPQA Diamond22.1
MUSR18.0
LiveCodeBench11.0
HLE5.2
AIME1.7
MATH-5001.7

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run llama3.2-vision:11b-instruct-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M1 (8GB)
8 GB VRAM • 68 GB/s
APPLE
$499
Apple M2 (8GB)
8 GB VRAM • 100 GB/s
APPLE
$599
Apple M3 (8GB)
8 GB VRAM • 100 GB/s
APPLE
$599
NVIDIA Tesla K8
8 GB VRAM • 160 GB/s
NVIDIA
NVIDIA Tesla M60
8 GB VRAM • 160 GB/s
NVIDIA

Find the best GPU for Llama-3.2-11B-Vision-Instruct

Build Hardware for Llama-3.2-11B-Vision-Instruct

Llama 3.2 11B Vision Instruct — instruction-tuned for visual Q&A tasks.

▸ SPEC SHEET

Llama-3.2-11B-Vision-Instruct11B Dense.

▸ SPECIFICATIONS
PARAMETERS
11B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
vision, chat
RELEASE DATE
2024-09-25
PROVIDER
Meta
FAMILY
llama
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M46.0 GB88%
Q3_K_L4.36.4 GB90%
IQ4_XS4.466.6 GB92%
Q4_K_S4.676.9 GB93%
Q4_K_M4.897.2 GB94%
Q5_K_S5.578.1 GB96%
Q5_K_M5.78.3 GB96%
Q6_K6.569.5 GB97%
Q8_08.512.2 GB100%
FP161622.5 GB100%
§ 01BENCHMARK SCORES
HumanEval45.0
MMLU-PRO55.0
MATH35.0
IFEval70.0
BBH55.0
MMMU50.7
GPQA35.0
MUSR18.0
MMBench76.8
LiveCodeBench11.0
AIME1.7
MATH-5001.7
GPQA Diamond22.1
HLE5.2
§ 02RUN COMMAND

Run Llama-3.2-11B-Vision-Instruct locally with Ollama — needs 7.2 GB VRAM at Q4_K_M:

$ollama run llama3.2-vision:11b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M