Model ID
vultr/VultronRetrieverCore-Qwen3.5-4.5B
Capabilities
VultronRetrieverCore Qwen3.5 4.5B
VultronRetrieverCore Qwen3.5 4.5B is a multimodal late-interaction retrieval model designed for visual document search and multilingual RAG across PDFs, scans, slides, and reports. It features 4.5B parameters, using a 32-layer hybrid GatedDeltaNet and full-attention architecture with 2,560 hidden size, 16 attention heads, and 320-dimensional multi-vector embeddings with MaxSim scoring. Supporting up to 262K context and 1,792 visual tokens, it delivers state-of-the-art 4–5B retrieval performance while balancing retrieval accuracy, latency, and memory efficiency.
About this model
VultronRetrieverCore Qwen3.5 4.5B is a multimodal late-interaction retrieval model designed for visual document search and multilingual RAG across PDFs, scans, slides, and reports. It features 4.5B parameters, using a 32-layer hybrid GatedDeltaNet and full-attention architecture with 2,560 hidden size, 16 attention heads, and 320-dimensional multi-vector embeddings with MaxSim scoring. Supporting up to 262K context and 1,792 visual tokens, it delivers state-of-the-art 4–5B retrieval performance while balancing retrieval accuracy, latency, and memory efficiency.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (vultr/VultronRetrieverCore-Qwen3.5-4.5B). Released under Apache 2.0.
Deploy Private Inference Endpoint
Choose your hardware and inference engine to get deployment commands and performance benchmarks tailored to your infrastructure
Hardware
MI325X
2,048 GB
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
Access Restricted
Enter your email to get access to this content
Performance under load
No benchmark data is available for this configuration yet.
AMD Instinct MI325X
Unlock Next-Generation AI Power on Vultr Cloud GPU