FitMyLLM
Shanghai AI Lab/Dense

Shanghai AI LabInternVL3 14B

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

chatvision
15.12B
Parameters
32K
Context length
8
Benchmarks
10
Quantizations
0
Architecture
Dense
Released
2025-04-15
Layers
48
KV Heads
8
Head Dim
128
Family
other

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
10.3 GB
8.0 + 2.3 KV
low
Q3_K_L4.3
10.9 GB
8.6 + 2.3 KV
moderate
IQ4_XS4.46
11.2 GB
8.9 + 2.3 KV
moderate
Q4_K_S4.67
11.6 GB
9.3 + 2.3 KV
moderate
Q4_K_M4.89
12.0 GB
9.7 + 2.3 KV
good
Q5_K_S5.57
13.3 GB
11.0 + 2.3 KV
good
Q5_K_M5.7
13.5 GB
11.3 + 2.3 KV
good
Q6_K6.56
15.1 GB
12.9 + 2.3 KV
excellent
Q8_08.5
18.8 GB
16.6 + 2.3 KV
lossless
FP1616
33.0 GB
30.7 + 2.3 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 (8)

MMBench85.6
MMMU67.1
IFEval65.0
BBH28.5
MMLU-PRO28.3
MATH12.2
MUSR11.7
GPQA6.5

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run other:15b-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.

NVIDIA CMP 170HX 10 GB
10 GB VRAM • 1560 GB/s
NVIDIA
NVIDIA CMP 50HX
10 GB VRAM • 560 GB/s
NVIDIA
NVIDIA CMP 90HX
10 GB VRAM • 760 GB/s
NVIDIA
NVIDIA Tesla K40c
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40d
12 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla K40m
12 GB VRAM • 288 GB/s
NVIDIA

Find the best GPU for InternVL3 14B

Build Hardware for InternVL3 14B
▸ SPEC SHEET

InternVL3 14B15.12B Dense.

▸ SPECIFICATIONS
PARAMETERS
15.12B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
32K tokens
CAPABILITIES
chat, vision
RELEASE DATE
2025-04-15
PROVIDER
Shanghai AI Lab
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M48.0 GB88%
Q3_K_L4.38.6 GB90%
IQ4_XS4.468.9 GB92%
Q4_K_S4.679.3 GB93%
Q4_K_M4.899.7 GB94%
Q5_K_S5.5711.0 GB96%
Q5_K_M5.711.3 GB96%
Q6_K6.5612.9 GB97%
Q8_08.516.6 GB100%
FP161630.7 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO28.3
MATH12.2
IFEval65.0
BBH28.5
MMMU67.1
GPQA6.5
MUSR11.7
MMBench85.6
§ 03COMPATIBLE GPUs
30 @ Q4_K_M