FitMyLLM
Shanghai AI Lab/Dense

Shanghai AI LabInternVL3 2B

We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance.

chatvision
2.09B
Parameters
32K
Context length
2
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2025-04-15
Layers
28
KV Heads
2
Head Dim
128
Family
other

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
2.1 GB
1.8 + 0.3 KV
good
Q5_K_S5.57
2.3 GB
1.9 + 0.3 KV
good
Q5_K_M5.7
2.3 GB
2.0 + 0.3 KV
good
Q6_K6.56
2.5 GB
2.2 + 0.3 KV
excellent
Q8_08.5
3.0 GB
2.7 + 0.3 KV
lossless
FP1616
5.0 GB
4.7 + 0.3 KV
lossless

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

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Community Ratings

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Benchmarks (2)

MMBench81.1
MMMU48.6

Run this model

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

Tag may need adjustment — check ollama.com/library/other for available tags.

▸ 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.

Find the best GPU for InternVL3 2B

Build Hardware for InternVL3 2B
▸ SPEC SHEET

InternVL3 2B2.09B Dense.

▸ SPECIFICATIONS
PARAMETERS
2.09B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, vision
RELEASE DATE
2025-04-15
PROVIDER
Shanghai AI Lab
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.891.8 GB94%
Q5_K_S5.571.9 GB96%
Q5_K_M5.72.0 GB96%
Q6_K6.562.2 GB97%
Q8_08.52.7 GB100%
FP16164.7 GB100%
§ 01BENCHMARK SCORES
MMMU48.6
MMBench81.1
§ 03COMPATIBLE GPUs
30 @ Q4_K_M