Jina AI/Dense

JJina Embeddings v4

jina-embeddings-v4 is a universal embedding model for multimodal and multilingual retrieval. The model is specially designed for complex document retrieval, including visually rich documents with charts, tables, and illustrations.

embeddingvisionmultilingual
3.8B
Parameters
125K
Context length
0
Benchmarks
6
Quantizations
0
Architecture
Dense
Released
2025-06-24
Layers
36
KV Heads
2
Head Dim
128
Family
embedding

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.892.8 GBgood
Q5_K_S5.573.1 GBgood
Q5_K_M5.73.2 GBgood
Q6_K6.563.6 GBexcellent
Q8_08.54.5 GBlossless
FP16168.1 GBlossless

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

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Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run embedding:4b-q4_K_M

Tag may need adjustment — check ollama.com/library/embedding 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 Tesla C2050
3 GB VRAM • 144 GB/s
NVIDIA
NVIDIA Tesla M2050
3 GB VRAM • 148 GB/s
NVIDIA
NVIDIA Tesla S2050
3 GB VRAM • 148 GB/s
NVIDIA

Find the best GPU for Jina Embeddings v4

Build Hardware for Jina Embeddings v4
▸ SPEC SHEET

Jina Embeddings v43.8B Dense.

▸ SPECIFICATIONS
PARAMETERS
3.8B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
125K tokens
CAPABILITIES
embedding, vision, multilingual
RELEASE DATE
2025-06-24
PROVIDER
Jina AI
FAMILY
embedding
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.892.8 GB94%
Q5_K_S5.573.1 GB96%
Q5_K_M5.73.2 GB96%
Q6_K6.563.6 GB97%
Q8_08.54.5 GB100%
FP16168.1 GB100%