Research Assistant: Paper Analysis on GPU

The Challenge: 15,000 Papers and Nobody Can Read Them All

A biomedical research institute affiliated with a London teaching hospital publishes and consumes roughly 15,000 research papers annually across oncology, immunology, and genomics. Each principal investigator tracks 200-400 papers in their subfield, but cross-disciplinary insights — a finding in immunology that has implications for a genomics trial — fall through the cracks. A postdoctoral researcher conducting a systematic review recently spent three weeks manually screening 4,800 abstracts to identify 230 relevant papers for a meta-analysis. The institute needs a tool that lets researchers query the entire corpus conversationally: “What dosing protocols have been used for pembrolizumab in combination with novel CTLA-4 inhibitors in Phase II trials published since 2023?”

The institute’s papers include pre-publication manuscripts, confidential clinical data references, and proprietary research directions. Uploading this corpus to external AI services is prohibited under the institute’s data governance policy and would risk exposing unpublished findings to competitors.

AI Solution: RAG-Powered Research Assistant

A retrieval-augmented generation (RAG) system combines a vector database of paper embeddings with an open-source LLM to answer research questions grounded in the institute’s literature. Papers are chunked, embedded, and indexed. When a researcher poses a question, the system retrieves the most relevant passages, feeds them to the LLM as context, and generates a sourced answer with citations to specific papers and sections.

Running the full pipeline — embedding model, vector database, and LLM — on a dedicated GPU server with vLLM provides instant responses while keeping all research data on UK infrastructure.

GPU Requirements

The RAG pipeline runs two GPU-intensive operations: real-time query embedding (lightweight) and LLM generation with retrieved context (heavy). For research queries requiring nuanced reasoning over complex biomedical text, a 13B-70B model provides the most reliable answers.

For an institute with 50 concurrent researchers, an RTX 6000 Pro or RTX 6000 Pro provides responsive performance. The RTX 6000 Pro’s 80 GB VRAM also enables running a larger 70B model (quantised) for superior reasoning on complex biomedical questions. Private AI hosting guarantees complete data sovereignty.

Recommended Stack

• vLLM serving LLaMA 3 70B (quantised) or Mixtral 8x7B for generation with strong reasoning capabilities.
• BGE-Large or PubMedBERT embeddings for domain-specific paper encoding.
• Qdrant or Chroma for the vector database, storing chunk-level embeddings with metadata (author, year, journal, section).
• LlamaIndex for the RAG orchestration layer with citation tracking.
• Gradio or Streamlit for the researcher-facing interface.

For processing scanned older papers, add document AI to extract text from PDF scans. Deploy a vision model to parse figures, charts, and microscopy images embedded in papers.

Cost Analysis

The three-week systematic review that prompted this project consumed 120 hours of postdoctoral researcher time, costing approximately £4,800 in salary. With the AI research assistant, the same screening task completes in 2-3 hours of interactive querying. Across the institute’s 80 active researchers, each saving an estimated 5 hours per week on literature searching, the annual productivity recovery exceeds £800,000.

The cross-disciplinary insight generation is harder to quantify but potentially more valuable. Surfacing a relevant finding from immunology that a genomics researcher would never have encountered manually could accelerate a research programme by months.

Getting Started

Compile your paper corpus in PDF format, including both published papers and internal pre-prints. Process through a PDF extraction pipeline, chunk into semantically meaningful passages, and embed using a biomedical-specific encoder. Deploy the RAG system to five power users for a two-week pilot, gathering feedback on answer quality and citation accuracy before rolling out institute-wide.

GigaGPU provides UK-based dedicated GPU servers for research AI workloads. Add an AI chatbot interface for natural-language research queries, or deploy additional models for automated literature screening.