## Abbreviations
\begin{table}
\begin{tabular}{c c} \hline \hline
**Image** & **Prompts** \\ \hline
1. & Identify any abnormalities in this chest X-ray, such as pneumonia, tuberculosis, or lung cancer. \\
2. & Diagnose and describe the next steps for a patient with the following X-ray. \\ \hline
1. & Locate and classify any brain tumors in this MRI scan. \\
2. & Outline the regions of the tumor in this MRI scan. \\ \hline
1. & Determine the stage of the kidney stone visible in this CT scan. \\
2. & What do you see in the CT Scan? \\ \hline
1. & Differentiate between melanoma, eczema, and psoriasis in this image. \\
2. & Does this skin condition suggest? \\ \hline
1. & Detect signs of diabetic retinopathy or macular degeneration in this retinal image. \\
2. & Classify abnormalities in this retinal image, such as retinal detachment or vascular occlusion. \\ \hline
1. & Locate cavities, impacted teeth, or signs of periodontal disease in this dental X-ray. \\
2. & Analyze this dental X-ray for signs of tooth decay or bone loss. \\ \hline
1. & Analyze this low-quality ultrasound image and provide a diagnosis. \\
2. & Identify abnormalities in this fetal ultrasound image. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Images and their corresponding prompts used for testing reponse times in consumer GPUs. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature** & **JAVIS (Fully Compliant)** & **RayLLM** & **OpenLLM** & **H2O LLM Studio** \\ \hline Target & Healthcare-focused & Internal network deployment & General-purpose developers & Developers/Researchers \\ Audience & ✓ (**Critical Feature**) & ✓ (Critical Feature) & �(**Critical** **Limation)** & �(**Critical** **Limation)** & �(**Critical** **Limation)** \\ Privacy and & **Cosed-network, HIPAA-like security & Closed-network, HIPAA-like setups & **Regquires external cloud setups** & **Regquires external cloud setups** \\ Long-Term & ✓ (**Critical Feature) Actively** & �(**Critical** **Limitation)** & ✓ (**Critical Feature) Actively** & ✓ (**Critical Feature) Actively** \\ Support & **Supported** & **Archived (May 2024)** & **Supported** & **Supported** \\ \multirow{2}{*}{Deployment Flexibility Stack} & ✓ Scales from PCs to hospitals (Entire Software Stack) & Scales from PCs to hospitals (Just Inference Engine) & �(**Only Available as Cloud Server Endpoint** \\ \multirow{2}{*}{Scaling Support Monitoring} & ✓ Multi-replica/Multi-model & ✓ Multi-replica/Multi-model & ✓ Online Only Multi-replica/Multi-model & �(**Lacks Scaling Support** \\ LLM/VLM Support & ✓ Multi-LLM/VLM & ✓ Multi-LLM/VLM & �(**Open-source LLMs only** & **Applied** \\ Monitoring & ✓ Advanced analytics + Ray dashboard & ✓ Just replica and status monitoring & ✓* Just static experiment monitoring & **Requires** \\ Tailoring and Customization & ✓ Fully customizable for hospital tailoring & �(**Non UI (just inference engine) & �(**Critical to pre-defined GUIs** & **Limited to pre-defined GUIs** \\ UI UI & Fully customizable UI & �(**Non UI (CLI-based only)) & �(**Basic Chat UI & ✓ Basic Chat UI (pre-defined) \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of JAVIS vs. alternative LLM deployment solutions. JAVIS is the only solution that successfully integrates on-premises, privacy-compliant AI deployment with multi-LLM/VLM support, real-time monitoring, and scalable orchestration via Ray Serve and VLM. Unlike OpenLLM and H2O LLM Studio, which rely on external cloud setups and lack hospital-wide scalability, JAVIS ensures full data privacy while providing a flexible and customizable architecture tailored to healthcare needs. Compared to RayLLM, JAVIS not only maintains the reliability of Ray Serve and VLM but also modernizes container maintenance for long-term support while introducing a user-friendly UI for streamlined hospital integration. JAVIS is the superior choice for deploying AI within healthcare institutions, overcoming the privacy, scalability, and maintenance barriers faced by competing solutions.